Artificial intelligence in the design, optimization, and performance prediction of concrete materials: a comprehensive review

Artificial intelligence in the design, optimization, and performance prediction of concrete materials: a comprehensive review


  • Monteiro, P. J., Miller, S. A. & Horvath, A. Towards sustainable concrete. Nat. Mater. 16, 698–699 (2017).

    PubMed 

    Google Scholar
     

  • Van Damme, H. Concrete material science: past, present, and future innovations. Cem. Concr. Res. 112, 5–24 (2018).


    Google Scholar
     

  • Moein, M. M. et al. Predictive models for concrete properties using machine learning and deep learning approaches: a review. J. Build. Eng. 63, 105444 (2023).


    Google Scholar
     

  • DeRousseau, M., Kasprzyk, J. & Srubar III, W. Computational design optimization of concrete mixtures: a review. Cem. Concr. Res. 109, 42–53 (2018).

    CAS 

    Google Scholar
     

  • ABrAMS, D. A. Design of concrete mixtures. Vol. 1 (Structural Materials Research Laboratory, Lewis Institute, 1919).

  • ACI Committee. Building code requirements for reinforced concrete (ACI 318-63). (1963).

  • Kosmatka, S. H. & Wilson, M. L. Design and control of concrete mixtures. (Portland Cement Association, Skokie, IL, USA, 2016).

  • Bharadwaj, K. et al. A new mixture proportioning method for performance-based concrete. ACI Mater. J. 119, 218 (2022).


    Google Scholar
     

  • Li, Z. et al. Machine learning in concrete science: applications, challenges, and best practices. npj Comput. Mater. 8, 127 (2022).


    Google Scholar
     

  • Ertel, W. Introduction to artificial intelligence. (Springer, 2018).

  • Kazemi, R. Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend. Eng. Rep. 5, e12676 (2023).


    Google Scholar
     

  • Fan, D. et al. Precise design and characteristics prediction of Ultra-High Performance Concrete (UHPC) based on artificial intelligence techniques. Cem. Concr. Compos. 122, 104171 (2021).

    CAS 

    Google Scholar
     

  • Adil, M., Ullah, R., Noor, S. & Gohar, N. Effect of number of neurons and layers in an artificial neural network for generalized concrete mix design. Neural Comput. Appl. 34, 8355–8363 (2022).


    Google Scholar
     

  • Feng, Y., Mohammadi, M., Wang, L., Rashidi, M. & Mehrabi, P. Application of artificial intelligence to evaluate the fresh properties of self-consolidating concrete. Materials 14, 4885 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nazar, S., Yang, J., Ahmad, A. & Shah, S. F. A. Comparative study of evolutionary artificial intelligence approaches to predict the rheological properties of fresh concrete. Mater. Today Commun. 32, 103964 (2022).

    CAS 

    Google Scholar
     

  • Haque, M. A., Chen, B., Javed, M. F. & Jalal, F. E. Evaluating the mechanical strength prediction performances of fly ash-based MPC mortar with artificial intelligence approaches. J. Clean. Prod. 355, 131815 (2022).

    CAS 

    Google Scholar
     

  • Mohamed, H. S. et al. Compressive behavior of elliptical concrete-filled steel tubular short columns using numerical investigation and machine learning techniques. Sci. Rep. 14, 27007 (2024).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yehia, S. A., Fayed, S., Zakaria, M. H. & Shahin, R. I. Prediction of RC T-Beams Shear Strength based on machine learning. Int. J. Concr. Struct. Mater. 18, 52 (2024).


    Google Scholar
     

  • Liu, Q. -f. et al. Prediction of chloride diffusivity in concrete using artificial neural network: Modelling and performance evaluation. Constr. Build. Mater. 268, 121082 (2021).

    CAS 

    Google Scholar
     

  • Liu, Y., Cao, Y., Wang, L., Chen, Z.-S. & Qin, Y. Prediction of the durability of high-performance concrete using an integrated RF-LSSVM model. Constr. Build. Mater. 356, 129232 (2022).

    CAS 

    Google Scholar
     

  • Amin, M. N. et al. Comparison of machine learning approaches with traditional methods for predicting the compressive strength of rice husk ash concrete. Crystals 11, 779 (2021).

    CAS 

    Google Scholar
     

  • Yehia, S. A., Shahin, R. I. & Fayed, S. Compressive behavior of eco-friendly concrete containing glass waste and recycled concrete aggregate using experimental investigation and machine learning techniques. Constr. Build. Mater. 436, 137002 (2024).


    Google Scholar
     

  • Alhalil, I. & Gullu, M. F. Predicting Main Characteristics of Reinforced Concrete Buildings Using Machine Learning. Buildings 14, 2967 (2024).


    Google Scholar
     

  • Habib, A., Junaid, M. T., Dirar, S., Barakat, S. & Al-Sadoon, Z. A. Machine learning-based estimation of reinforced concrete columns stiffness modifiers for improved accuracy in linear response history analysis. J. Earthq. Eng. 29, 130–155 (2025).


    Google Scholar
     

  • Chen, X., Zhang, X. & Chen, W.-Z. Advanced predictive modeling of concrete compressive strength and slump characteristics: a comparative evaluation of BPNN, SVM, and RF models optimized via PSO. Materials 17, 4791 (2024).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ramkumar, K., Rajkumar, P. K., Ahmmad, S. N. & Jegan, M. A review on performance of self-compacting concrete–use of mineral admixtures and steel fibres with artificial neural network application. Constr. Build. Mater. 261, 120215 (2020).

    CAS 

    Google Scholar
     

  • Fan, D. et al. Intelligent design and manufacturing of ultra-high performance concrete (UHPC)–a review. Constr. Build. Mater. 385, 131495 (2023).

    CAS 

    Google Scholar
     

  • Fan, D. et al. Intelligent predicting and monitoring of ultra-high-performance fiber reinforced concrete composites−a review. Compos. Part A Appl. Sci. Manufactur. 188, 108555 (2024).

  • Nunez, I., Marani, A., Flah, M. & Nehdi, M. L. Estimating compressive strength of modern concrete mixtures using computational intelligence: a systematic review. Constr. Build. Mater. 310, 125279 (2021).


    Google Scholar
     

  • Rathnayaka, M. et al. Machine learning approaches to predict compressive strength of fly ash-based geopolymer concrete: a comprehensive review. Constr. Build. Mater. 419, 135519 (2024).

    CAS 

    Google Scholar
     

  • Jia, H., Qiao, G. & Han, P. Machine learning algorithms in the environmental corrosion evaluation of reinforced concrete structures-a review. Cem. Concr. Compos. 133, 104725 (2022).

    CAS 

    Google Scholar
     

  • Liu, K. et al. Frost resistance of recycled aggregate concrete: a critical review. J. Build. Eng. 109450 (2024).

  • Nazar, S. et al. An evolutionary machine learning-based model to estimate the rheological parameters of fresh concrete. Structures 48, 1670–1683 (2023).


    Google Scholar
     

  • Behnood, A. & Golafshani, E. M. Machine learning study of the mechanical properties of concretes containing waste foundry sand. Constr. Build. Mater. 243, 118152 (2020).


    Google Scholar
     

  • Taffese, W. Z. & Sistonen, E. Machine learning for durability and service-life assessment of reinforced concrete structures: recent advances and future directions. Autom. Constr. 77, 1–14 (2017).


    Google Scholar
     

  • Mirzazade, A. et al. Semi-autonomous inspection for concrete structures using digital models and a hybrid approach based on deep learning and photogrammetry. J. Civ. Struct. Health Monit. 13, 1633–1652 (2023).


    Google Scholar
     

  • Amjad, H., Khattak, M. M. H. & Khushnood, R. A. A simplified machine learning empirical model for biomimetic crack healing of bio-inspired concrete. Mater. Today Commun. 37, 107063 (2023).

    CAS 

    Google Scholar
     

  • Altayeb, M. et al. AI Agents for UHPC experimental design: High strength and low cost with fewer experimental trials. Constr. Build. Mater. 416, 135206 (2024).

    CAS 

    Google Scholar
     

  • Zain, M. F. M., Islam, M. N. & Basri, I. H. An expert system for mix design of high performance concrete. Adv. Eng. Softw. 36, 325–337 (2005).


    Google Scholar
     

  • Bui, D.-K., Nguyen, T., Chou, J.-S., Nguyen-Xuan, H. & Ngo, T. D. A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete. Constr. Build. Mater. 180, 320–333 (2018).


    Google Scholar
     

  • Boudreaux, P. et al. A rule-based expert system applied to moisture durability of building envelopes. J. Build. Phys. 42, 416–437 (2018).


    Google Scholar
     

  • Xiaowei, W. in Proceedings of the 2011, International Conference on Informatics, Cybernetics, and Computer Engineering (ICCE2011) November 19–20, 2011, Melbourne, Australia: Volume 2: Information Systems and Computer Engineering. 273-280 (Springer, 2011).

  • Fonseca, D. J., Moynihan, G. P. & Richards, E. P. Expert System for Industrial Waste Recycling In Road Construction. University Transportation Center for Alabama The University of Alabama, The University of Alabama in Birmingham, and The University of Alabama at Huntsville (2004).

  • Neshat, M., Adeli, A., Sepidnam, G. & Sargolzaei, M. Predication of concrete mix design using adaptive neural fuzzy inference systems and fuzzy inference systems. Int. J. Adv. Manuf. Technol. 63, 373–390 (2012).


    Google Scholar
     

  • Beycioğlu, A., Gültekin, A. & Aruntaş, H. Usability of fuzzy logic modeling for prediction of fresh properties of self-compacting concrete. Acta Phys. Polonica A 132, 1140–1141 (2017).


    Google Scholar
     

  • Tanyildizi, H. Fuzzy logic model for prediction of mechanical properties of lightweight concrete exposed to high temperature. Mater. Des. 30, 2205–2210 (2009).

    CAS 

    Google Scholar
     

  • Nehdi, M. & Bassuoni, M. Fuzzy logic approach for estimating durability of concrete. Proc. Inst. Civ. Eng.-Constr. Mater. 162, 81–92 (2009).


    Google Scholar
     

  • Mohamed, M. & Tran, D. Q. Risk-based inspection for concrete pavement construction using fuzzy sets and Bayesian networks. Autom. Constr. 128, 103761 (2021).


    Google Scholar
     

  • Topçu, İ. B. & Sarıdemir, M. Prediction of rubberized concrete properties using artificial neural network and fuzzy logic. Constr. Build. Mater. 22, 532–540 (2008).


    Google Scholar
     

  • Moon, S., Lee, G. & Chi, S. Automated system for construction specification review using natural language processing. Adv. Eng. Inform. 51, 101495 (2022).


    Google Scholar
     

  • Kumar, A., Bakshi, B. R., Ramteke, M. & Kodamana, H. Recycle-BERT: extracting knowledge about plastic waste recycling by natural language processing. ACS Sustain. Chem. Eng. 11, 12123–12134 (2023).

    CAS 

    Google Scholar
     

  • Völker, C., Rug, T., Jablonka, K. M. & Kruschwitz, S. LLMs can design sustainable concrete–a systematic benchmark. https://doi.org/10.21203/rs.3.rs-3913272/v1 (Preprint 2024).

  • Xu, Z. et al. Robotics technologies aided for 3D printing in construction: a review. Int. J. Adv. Manuf. Technol. 118, 3559–3574 (2022).


    Google Scholar
     

  • Gucunski, N. et al. in Structural Health Monitoring and Inspection of Advanced Materials, Aerospace, and Civil Infrastructure 2015. 162-173 (SPIE, 2015).

  • Narloch, P. et al. Predicting compressive strength of cement-stabilized rammed earth based on SEM images using computer vision and deep learning. Appl. Sci. 9, 5131 (2019).

    CAS 

    Google Scholar
     

  • Asadi, P., Gindy, M., Alvarez, M. & Asadi, A. A computer vision based rebar detection chain for automatic processing of concrete bridge deck GPR data. Autom. Constr. 112, 103106 (2020).


    Google Scholar
     

  • Davoudi, R., Miller, G. R., Calvi, P. & Kutz, J. N. Computer vision–based damage and stress state estimation for reinforced concrete and steel fiber–reinforced concrete panels. Struct. Health Monit. 19, 1645–1665 (2020).


    Google Scholar
     

  • Ahn, E. et al. Monitoring of self-healing in concrete with micro-capsules using a combination of air-coupled surface wave and computer-vision techniques. Struct. Health Monit. 21, 1661–1677 (2022).


    Google Scholar
     

  • Topcu, I. B. & Sarıdemir, M. Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Comput. Mater. Sci. 41, 305–311 (2008).

    CAS 

    Google Scholar
     

  • Asteris, P. G., Skentou, A. D., Bardhan, A., Samui, P. & Pilakoutas, K. Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models. Cem. Concr. Res. 145, 106449 (2021).

    CAS 

    Google Scholar
     

  • Whang, S. E., Roh, Y., Song, H. & Lee, J.-G. Data collection and quality challenges in deep learning: a data-centric AI perspective. VLDB J. 32, 791–813 (2023).


    Google Scholar
     

  • Lyngdoh, G. A., Zaki, M., Krishnan, N. A. & Das, S. Prediction of concrete strengths enabled by missing data imputation and interpretable machine learning. Cem. Concr. Compos. 128, 104414 (2022).

    CAS 

    Google Scholar
     

  • Sun, Y. & Gu, Z. Using computer vision to recognize construction material: a trustworthy dataset perspective. Resour., Conserv. Recycl.183, 106362 (2022).


    Google Scholar
     

  • Shahrokhishahraki, M., Malekpour, M., Mirvalad, S. & Faraone, G. Machine learning predictions for optimal cement content in sustainable concrete constructions. J. Build. Eng. 82, 108160 (2024).


    Google Scholar
     

  • Zheng, W., Shui, Z., Xu, Z., Gao, X. & Zhang, S. Multi-objective optimization of concrete mix design based on machine learning. J. Build. Eng. 76, 107396 (2023).


    Google Scholar
     

  • Ahmad, S. A., Rafiq, S. K., Ahmed, H. U., Abdulrahman, A. S. & Ramezanianpour, A. M. Innovative soft computing techniques including artificial neural network and nonlinear regression models to predict the compressive strength of environmentally friendly concrete incorporating waste glass powder. Innov. Infrastruct. Solut. 8, 119 (2023).


    Google Scholar
     

  • Ahmad, S. A. et al. Predicting concrete strength with waste glass using statistical evaluations, neural networks, and linear/nonlinear models. Asian J. Civ. Eng. 24, 3023–3035 (2023).


    Google Scholar
     

  • Yeh, I.-C. Design of high-performance concrete mixture using neural networks and nonlinear programming. J. Comput. Civ. Eng. 13, 36–42 (1999).


    Google Scholar
     

  • Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).


    Google Scholar
     

  • Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).


    Google Scholar
     

  • Sun, Y. et al. Prediction & optimization of alkali-activated concrete based on the random forest machine learning algorithm. Constr. Build. Mater. 385, 131519 (2023).

    CAS 

    Google Scholar
     

  • Ji, T., Lin, T. & Lin, X. A concrete mix proportion design algorithm based on artificial neural networks. Cem. Concr. Res. 36, 1399–1408 (2006).

    CAS 

    Google Scholar
     

  • Yue, L., Hongwen, L., Yinuo, L. & Caiyun, J. Optimum design of high-strength concrete mix proportion for crack resistance using artificial neural networks and genetic algorithm. Front. Mater. 7, 590661 (2020).


    Google Scholar
     

  • Cortes, C. Support-Vector Networks. Machine Learning (1995).

  • Huang, Y., Zhang, J., Ann, F. T. & Ma, G. Intelligent mixture design of steel fibre reinforced concrete using a support vector regression and firefly algorithm based multi-objective optimization model. Constr. Build. Mater. 260, 120457 (2020).


    Google Scholar
     

  • Smola, A. J. & Schölkopf, B. A tutorial on support vector regression. Stat. Comput. 14, 199–222 (2004).


    Google Scholar
     

  • Wu, Z., Chen, Y. & Luo, D. Comparative study of five machine learning algorithms on prediction of the height of the water-conducting fractured zone in undersea mining. Sci. Rep. 14, 21047 (2024).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Duan, K., Keerthi, S. S. & Poo, A. N. Evaluation of simple performance measures for tuning SVM hyperparameters. Neurocomputing 51, 41–59 (2003).


    Google Scholar
     

  • Jayaram, M., Nataraja, M. & Ravikumar, C. Design of high performance concrete mixes through particle swarm optimization. J. Intell. Syst. 19, 249–264 (2010).


    Google Scholar
     

  • Saleh, E., Tarawneh, A., Naser, M., Abedi, M. & Almasabha, G. You only design once (YODO): Gaussian Process-Batch Bayesian optimization framework for mixture design of ultra high performance concrete. Constr. Build. Mater. 330, 127270 (2022).


    Google Scholar
     

  • Hosseini Sarcheshmeh, A., Etemadfard, H., Najmoddin, A. & Ghalehnovi, M. Hyperparameters’ role in machine learning algorithm for modeling of compressive strength of recycled aggregate concrete. Innov. Infrastruct. Solut. 9, 212 (2024).


    Google Scholar
     

  • Hsu, C., Chang, C. & Lin, C. A Practical Guide to Support Vector Classification. (2003).

  • Yu, Y., Zhang, C., Gu, X. & Cui, Y. Expansion prediction of alkali aggregate reactivity-affected concrete structures using a hybrid soft computing method. Neural Comput. Appl. 31, 8641–8660 (2019).


    Google Scholar
     

  • Chaabene, W. B., Flah, M. & Nehdi, M. L. Machine learning prediction of mechanical properties of concrete: critical review. Constr. Build. Mater. 260, 119889 (2020).


    Google Scholar
     

  • Hossin, M. & Sulaiman, M. N. A review on evaluation metrics for data classification evaluations. Int J Data Min Knowl Manag Process 5, 1 (2015).


    Google Scholar
     

  • Golafshani, E. M., Arashpour, M. & Kashani, A. Green mix design of rubbercrete using machine learning-based ensemble model and constrained multi-objective optimization. J. Clean. Prod. 327, 129518 (2021).


    Google Scholar
     

  • Naseri, H., Jahanbakhsh, H., Hosseini, P. & Nejad, F. M. Designing sustainable concrete mixture by developing a new machine learning technique. J. Clean. Prod. 258, 120578 (2020).


    Google Scholar
     

  • Ghoddousi, P., Shirzadi Javid, A. A. & Sobhani, J. A fuzzy system methodology for concrete mixture design considering maximum packing density and minimum cement content. Arab. J. Sci. Eng. 40, 2239–2249 (2015).

    CAS 

    Google Scholar
     

  • Mohd Adnan, M., Sarkheyli, A., Mohd Zain, A. & Haron, H. Fuzzy logic for modeling machining process: a review. Artif. Intell. Rev. 43, 345–379 (2015).


    Google Scholar
     

  • Hassan, F. u. & Le, T. Automated requirements identification from construction contract documents using natural language processing. J. Leg. Aff. Disput. Resolut. Eng. Constr. 12, 04520009 (2020).

  • Xu, N., Ma, L., Wang, L., Deng, Y. & Ni, G. Extracting domain knowledge elements of construction safety management: rule-based approach using Chinese natural language processing. J. Manag. Eng. 37, 04021001 (2021).


    Google Scholar
     

  • Gomaa, E., Han, T., ElGawady, M., Huang, J. & Kumar, A. Machine learning to predict properties of fresh and hardened alkali-activated concrete. Cem. Concr. Compos. 115, 103863 (2021).

    CAS 

    Google Scholar
     

  • Oey, T., Jones, S., Bullard, J. W. & Sant, G. Machine learning can predict setting behavior and strength evolution of hydrating cement systems. J. Am. Ceram. Soc. 103, 480–490 (2020).

    CAS 

    Google Scholar
     

  • ASTM C143, Standard Test Method for Slump of Hydraulic-Cement Concrete. 04.02 (2020).

  • ASTM C230/C230M, Standard specification for flow table for use in tests of hydraulic cement. 1 (2020).

  • ASTM C1611, Standard Test Method for Slump Flow of Self-Consolidating Concrete. 04.02 (2021).

  • ASTM C1261/C1621M, Standard Test Method for Passing Ability of Self-Consolidating Concrete by J-Ring. 04.02 (2023).

  • González-Taboada, I., González-Fonteboa, B., Martínez-Abella, F. & Carro-López, D. Self-compacting recycled concrete: relationships between empirical and rheological parameters and proposal of a workability box. Constr. Build. Mater. 143, 537–546 (2017).


    Google Scholar
     

  • Anand Kumar, B. in International Conference on Civil Engineering Trends and Challenges for Sustainability. 39-55 (Springer, 2025).

  • Wang, X., Taylor, P. & Wang, X. A novel test to determine the workability of slipform concrete mixtures. Mag. Concr. Res. 69, 292–305 (2017).


    Google Scholar
     

  • Yang, L., An, X. & Du, S. Estimating workability of concrete with different strength grades based on deep learning. Measurement 186, 110073 (2021).


    Google Scholar
     

  • el Mahdi Safhi, A., Dabiri, H., Soliman, A. & Khayat, K. H. Prediction of self-consolidating concrete properties using XGBoost machine learning algorithm: part 1–workability. Constr. Build. Mater. 408, 133560 (2023).


    Google Scholar
     

  • Hoang, N.-D. & Pham, A.-D. Estimating concrete workability based on slump test with least squares support vector regression. J. Constr. Eng. 2016, 5089683 (2016).


    Google Scholar
     

  • Cenik, H. Comprehensive Concrete Slump and Strength Dataset (2023).

  • Yeh, I.-C. Concrete Compressive Strength [Dataset]. UCI Machine Learning Repository, https://doi.org/10.24432/C5PK67 (1998).

  • Mandal, R., Panda, S. K. & Nayak, S. Rheology of concrete: critical review, recent advancements, and future prospectives. Constr. Build. Mater. 392, 132007 (2023).

    CAS 

    Google Scholar
     

  • Khayat, K. H., Meng, W., Vallurupalli, K. & Teng, L. Rheological properties of ultra-high-performance concrete—an overview. Cem. Concr. Res. 124, 105828 (2019).

    CAS 

    Google Scholar
     

  • Morrison, F. A. Understanding rheology. Vol. 1 (Oxford University Press, 2001).

  • Hu, J. & Wang, K. Effect of coarse aggregate characteristics on concrete rheology. Constr. Build. Mater. 25, 1196–1204 (2011).


    Google Scholar
     

  • Tattersall, G. H. & Banfill, P. F. The rheology of fresh concrete. (1983).

  • Roussel, N. A thixotropy model for fresh fluid concretes: Theory, validation and applications. Cem. Concr. Res. 36, 1797–1806 (2006).

    CAS 

    Google Scholar
     

  • Rahul, A., Santhanam, M., Meena, H. & Ghani, Z. 3D printable concrete: mixture design and test methods. Cem. Concr. Compos. 97, 13–23 (2019).

    CAS 

    Google Scholar
     

  • Yahyaei, B., Asadollahfardi, G., Salehi, A. M. & Esmaeili, N. Study of shear-thickening and shear-thinning behavior in rheology of self-compacting concrete with micro-nano bubble. Struct. Concr. 23, 1920–1932 (2022).


    Google Scholar
     

  • Liu, G., Cheng, W., Chen, L., Pan, G. & Liu, Z. Rheological properties of fresh concrete and its application on shotcrete. Constr. Build. Mater. 243, 118180 (2020).


    Google Scholar
     

  • Chen, L., Ma, G., Liu, G. & Liu, Z. Effect of pumping and spraying processes on the rheological properties and air content of wet-mix shotcrete with various admixtures. Constr. Build. Mater. 225, 311–323 (2019).


    Google Scholar
     

  • Wallevik, O. H. & Wallevik, J. E. Rheology as a tool in concrete science: the use of rheographs and workability boxes. Cem. Concr. Res. 41, 1279–1288 (2011).

    CAS 

    Google Scholar
     

  • De Larrard, F., Ferraris, C. & Sedran, T. Fresh concrete: a Herschel-Bulkley material. Mater. Struct. 31, 494–498 (1998).


    Google Scholar
     

  • Feys, D., Wallevik, J. E., Yahia, A., Khayat, K. H. & Wallevik, O. H. Extension of the Reiner–Riwlin equation to determine modified Bingham parameters measured in coaxial cylinders rheometers. Mater. Struct. 46, 289–311 (2013).

    CAS 

    Google Scholar
     

  • Skare, E. L. et al. Rheology modelling of cement paste with manufactured sand and silica fume: Comparing suspension models with artificial neural network predictions. Constr. Build. Mater. 317, 126114 (2022).

    CAS 

    Google Scholar
     

  • Nguyen, T.-D., Tran, T.-H. & Hoang, N.-D. Prediction of interface yield stress and plastic viscosity of fresh concrete using a hybrid machine learning approach. Adv. Eng. Inform. 44, 101057 (2020).


    Google Scholar
     

  • Chen, Y. & Odler, I. On the origin of Portland cement setting. Cem. Concr. Res. 22, 1130–1140 (1992).

    CAS 

    Google Scholar
     

  • Nuhu, S., Ladan, S. & Muhammad, A. U. Effects and control of chemical composition of clinker for cement production. Int. J. Control Sci. Eng. 10, 16–21 (2020).


    Google Scholar
     

  • Uchikawa, H., Hanehara, S., Shirasaka, T. & Sawaki, D. Effect of admixture on hydration of cement, adsorptive behavior of admixture and fluidity and setting of fresh cement paste. Cem. Concr. Res. 22, 1115–1129 (1992).

    CAS 

    Google Scholar
     

  • Eren, O., Brooks, J. J. & Celik, T. Setting times of fly ash and slag-cement concretes as affected by curing temperature. Cem. Concr. Aggreg. 17, 11–17 (1995).

    CAS 

    Google Scholar
     

  • Li, L., Wang, R. & Zhang, S. Effect of curing temperature and relative humidity on the hydrates and porosity of calcium sulfoaluminate cement. Constr. Build. Mater. 213, 627–636 (2019).

    CAS 

    Google Scholar
     

  • Hu, J., Ge, Z. & Wang, K. Influence of cement fineness and water-to-cement ratio on mortar early-age heat of hydration and set times. Constr. Build. Mater. 50, 657–663 (2014).


    Google Scholar
     

  • Burris, L. E. & Kurtis, K. E. Water-to-cement ratio of calcium sulfoaluminate belite cements: Hydration, setting time, and strength development. Cement 8, 100032 (2022).

    CAS 

    Google Scholar
     

  • Ghoddousi, P., Shirzadi Javid, A. A., Sobhani, J. & Zaki Alamdari, A. A new method to determine initial setting time of cement and concrete using plate test. Mater. Struct. 49, 3135–3142 (2016).

    CAS 

    Google Scholar
     

  • Kang, X., Lei, H. & Xia, Z. A comparative study of modified fall cone method and semi-adiabatic calorimetry for measurement of setting time of cement based materials. Constr. Build. Mater. 248, 118634 (2020).


    Google Scholar
     

  • Xu, Q., Hu, J., Ruiz, J. M., Wang, K. & Ge, Z. Isothermal calorimetry tests and modeling of cement hydration parameters. Thermochim. Acta 499, 91–99 (2010).

    CAS 

    Google Scholar
     

  • Lee, T. & Lee, J. Setting time and compressive strength prediction model of concrete by nondestructive ultrasonic pulse velocity testing at early age. Constr. Build. Mater. 252, 119027 (2020).

    CAS 

    Google Scholar
     

  • Yousuf, F., Wei, X. & Zhou, J. Monitoring the setting and hardening behaviour of cement paste by electrical resistivity measurement. Constr. Build. Mater. 252, 118941 (2020).

    CAS 

    Google Scholar
     

  • Tsardaka, E.-C., Sougioultzi, K., Konstantinidis, A. & Stefanidou, M. Interpreting the setting time of cement pastes for modelling mechanical properties. Case Study Constr. Mater. 19, e02364 (2023).


    Google Scholar
     

  • Güneyisi, E., Gesoglu, M. & Özbay, E. Evaluating and forecasting the initial and final setting times of self-compacting concretes containing mineral admixtures by neural network. Mater. Struct. 42, 469–484 (2009).


    Google Scholar
     

  • Baseri, H., Rabiee, S., Moztarzadeh, F. & Solati-Hashjin, M. Mechanical strength and setting times estimation of hydroxyapatite cement by using neural network. Mater. Des. 31, 2585–2591 (2010).

    CAS 

    Google Scholar
     

  • Akpinar, P. & Abubakar, M. A. in International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions. 950-957 (Springer, 2020).

  • Guo, J., Wang, L., Fan, K. & Yang, B. An efficient model for predicting setting time of cement based on broad learning system. Appl. Soft Comput. 96, 106698 (2020).


    Google Scholar
     

  • Liu, J., Tang, L., Li, D., Cui, X. & Kang, W. Data-driven models to predict the water-to-cement ratio and initial setting time of cement grouts. Nondestruct. Test. Eval. 1-20 (2024).

  • Gulbandilar, E. & Kocak, Y. Prediction of the effects of fly ash and silica fume on the setting time of Portland cement with fuzzy logic. Neural Comput. Appl. 22, 1485–1491 (2013).


    Google Scholar
     

  • Kocaka, Y., Gulbandilarb, E. & Alpaslan, L. Prediction the effects of blast furnace slag and waste tire rubber powder on the setting time of Portland cement with fuzzy logic. Mater. Methods Technol. 9, 298–307 (2015).


    Google Scholar
     

  • Smith, G. N. Probability and statistics in civil engineering. Collins professional and technical books 244 (1986).

  • Pal, A., Ahmed, K. S. & Mangalathu, S. Data-driven machine learning approaches for predicting slump of fiber-reinforced concrete containing waste rubber and recycled aggregate. Constr. Build. Mater. 417, 135369 (2024).


    Google Scholar
     

  • Liu, Y. et al. Differential evolution–based integrated model for predicting concrete slumps. Eng. Sci. Technol., Int. J. 51, 101655 (2024).


    Google Scholar
     

  • Aldea, C.-M., Young, F., Wang, K. & Shah, S. P. Effects of curing conditions on properties of concrete using slag replacement. Cem. Concr. Res. 30, 465–472 (2000).

    CAS 

    Google Scholar
     

  • Neville, A. M. & Brooks, J. J. Concrete technology. Vol. 438 (Longman Scientific & Technical England, 1987).

  • Elshaarawy, M. K., Alsaadawi, M. M. & Hamed, A. K. Machine learning and interactive GUI for concrete compressive strength prediction. Sci. Rep. 14, 16694 (2024).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shafighfard, T., Kazemi, F., Asgarkhani, N. & Yoo, D.-Y. Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete. Eng. Appl. Artif. Intell. 136, 109053 (2024).


    Google Scholar
     

  • Chou, J.-S. & Pham, A.-D. Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength. Constr. Build. Mater. 49, 554–563 (2013).


    Google Scholar
     

  • Adeodato, P. J., Arnaud, A. L., Vasconcelos, G. C., Cunha, R. C. & Monteiro, D. S. MLP ensembles improve long term prediction accuracy over single networks. Int. J. Forecast. 27, 661–671 (2011).


    Google Scholar
     

  • Feng, D.-C. et al. Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach. Constr. Build. Mater. 230, 117000 (2020).


    Google Scholar
     

  • Wu, Y., Pieralisi, R., Sandoval, F. G. B., López-Carreño, R.-D. & Pujadas, P. Optimizing pervious concrete with machine learning: Predicting permeability and compressive strength using artificial neural networks. Constr. Build. Mater. 443, 137619 (2024).


    Google Scholar
     

  • Song, Y. et al. Prediction of compressive strength of fly-ash-based concrete using ensemble and non-ensemble supervised machine-learning approaches. Appl. Sci. 12, 361 (2021).


    Google Scholar
     

  • Hosseinzadeh, M., Samadvand, H., Hosseinzadeh, A., Mousavi, S. S. & Dehestani, M. Concrete strength and durability prediction through deep learning and artificial neural networks. Front. Struct. Civ. Eng. 18, 1540–1555 (2024).

    CAS 

    Google Scholar
     

  • Young, B. A., Hall, A., Pilon, L., Gupta, P. & Sant, G. Can the compressive strength of concrete be estimated from knowledge of the mixture proportions? New insights from statistical analysis and machine learning methods. Cem. Concr. Res. 115, 379–388 (2019).

    CAS 

    Google Scholar
     

  • Song, H. et al. Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms. Constr. Build. Mater. 308, 125021 (2021).

    CAS 

    Google Scholar
     

  • DeRousseau, M. A., Laftchiev, E., Kasprzyk, J. R., Rajagopalan, B. & Srubar, W. III A comparison of machine learning methods for predicting the compressive strength of field-placed concrete. Constr. Build. Mater. 228, 116661 (2019).


    Google Scholar
     

  • Abuodeh, O. R., Abdalla, J. A. & Hawileh, R. A. Assessment of compressive strength of Ultra-high Performance Concrete using deep machine learning techniques. Appl. Soft Comput. 95, 106552 (2020).


    Google Scholar
     

  • Chopra, P., Sharma, R. K., Kumar, M. & Chopra, T. Comparison of machine learning techniques for the prediction of compressive strength of concrete. Adv. Civ. Eng. 2018, 5481705 (2018).


    Google Scholar
     

  • Shen, Z., Deifalla, A. F., Kamiński, P. & Dyczko, A. Compressive strength evaluation of ultra-high-strength concrete by machine learning. Materials 15, 3523 (2022).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Paudel, S., Pudasaini, A., Shrestha, R. K. & Kharel, E. Compressive strength of concrete material using machine learning techniques. Clean. Eng. Technol. 15, 100661 (2023).


    Google Scholar
     

  • Ahmad, A. et al. Comparative study of supervised machine learning algorithms for predicting the compressive strength of concrete at high temperature. Materials 14, 4222 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ahmad, A. et al. Prediction of geopolymer concrete compressive strength using novel machine learning algorithms. Polymers 13, 3389 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chen, H. et al. Compressive strength prediction of high-strength concrete using long short-term memory and machine learning algorithms. Buildings 12, 302 (2022).


    Google Scholar
     

  • Dabiri, H., Kioumarsi, M., Kheyroddin, A., Kandiri, A. & Sartipi, F. Compressive strength of concrete with recycled aggregate; a machine learning-based evaluation. Clean. Mater. 3, 100044 (2022).


    Google Scholar
     

  • Xu, Y. et al. Computation of high-performance concrete compressive strength using standalone and ensembled machine learning techniques. Materials 14, 7034 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, D., Tang, Z., Kang, Q., Zhang, X. & Li, Y. Machine learning-based method for predicting compressive strength of concrete. Processes 11, 390 (2023).

    CAS 

    Google Scholar
     

  • de-Prado-Gil, J., Palencia, C., Silva-Monteiro, N. & Martínez-García, R. To predict the compressive strength of self compacting concrete with recycled aggregates utilizing ensemble machine learning models. Case Study Constr. Mater. 16, e01046 (2022).


    Google Scholar
     

  • Ahmad, W. et al. Application of advanced machine learning approaches to predict the compressive strength of concrete containing supplementary cementitious materials. Materials 14, 5762 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wan, Z., Xu, Y. & Šavija, B. On the use of machine learning models for prediction of compressive strength of concrete: influence of dimensionality reduction on the model performance. Materials 14, 713 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Salami, B. A., Olayiwola, T., Oyehan, T. A. & Raji, I. A. Data-driven model for ternary-blend concrete compressive strength prediction using machine learning approach. Constr. Build. Mater. 301, 124152 (2021).


    Google Scholar
     

  • Li, Y. et al. Compressive strength of steel fiber-reinforced concrete employing supervised machine learning techniques. Materials 15, 4209 (2022).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mansouri, E., Manfredi, M. & Hu, J.-W. Environmentally friendly concrete compressive strength prediction using hybrid machine learning. Sustainability 14, 12990 (2022).

    CAS 

    Google Scholar
     

  • Iqtidar, A. et al. Prediction of compressive strength of rice husk ash concrete through different machine learning processes. Crystals 11, 352 (2021).

    CAS 

    Google Scholar
     

  • Zeng, Z. et al. Accurate prediction of concrete compressive strength based on explainable features using deep learning. Constr. Build. Mater. 329, 127082 (2022).


    Google Scholar
     

  • Wu, Y. & Zhou, Y. Hybrid machine learning model and Shapley additive explanations for compressive strength of sustainable concrete. Constr. Build. Mater. 330, 127298 (2022).


    Google Scholar
     

  • Shamsabadi, E. A. et al. Machine learning-based compressive strength modelling of concrete incorporating waste marble powder. Constr. Build. Mater. 324, 126592 (2022).


    Google Scholar
     

  • Meisuh, B. K., Kankam, C. K. & Buabin, T. K. Effect of quarry rock dust on the flexural strength of concrete. Case Study Constr. Mater. 8, 16–22 (2018).


    Google Scholar
     

  • Vargas, J. F. et al. Machine-Learning-Based Predictive Models for Compressive Strength, Flexural Strength, and Slump of Concrete. Appl. Sci. 14, 4426 (2024).

    CAS 

    Google Scholar
     

  • Mehta, V. Machine learning approach for predicting concrete compressive, splitting tensile, and flexural strength with waste foundry sand. J. Build. Eng. 70, 106363 (2023).


    Google Scholar
     

  • Li, Y., Liu, Y., Lin, H. & Jin, C. Study of flexural strength of concrete containing mineral admixtures based on machine learning. Sci. Rep. 13, 18061 (2023).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shah, H. A. et al. Application of machine learning techniques for predicting compressive, splitting tensile, and flexural strengths of concrete with metakaolin. Materials 15, 5435 (2022).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kang, M.-C., Yoo, D.-Y. & Gupta, R. Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete. Constr. Build. Mater. 266, 121117 (2021).


    Google Scholar
     

  • Zheng, D. et al. Flexural strength prediction of steel fiber-reinforced concrete using artificial intelligence. Materials 15, 5194 (2022).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ali, A. et al. Machine learning-based predictive model for tensile and flexural strength of 3D-printed concrete. Materials 16, 4149 (2023).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Uddin, M. N., Ye, J., Deng, B., Li, L. -z. & Yu, K. Interpretable machine learning for predicting the strength of 3D printed fiber-reinforced concrete (3DP-FRC). J. Build. Eng. 72, 106648 (2023).


    Google Scholar
     

  • Qian, Y., Sufian, M., Hakamy, A., Farouk Deifalla, A. & El-said, A. Application of machine learning algorithms to evaluate the influence of various parameters on the flexural strength of ultra-high-performance concrete. Front. Mater. 9, 1114510 (2023).


    Google Scholar
     

  • Wang, Q., Hussain, A., Farooqi, M. U. & Deifalla, A. F. Artificial intelligence-based estimation of ultra-high-strength concrete’s flexural property. Case Study Constr. Mater. 17, e01243 (2022).


    Google Scholar
     

  • Jin, L., Yu, W. & Du, X. Size effect on static splitting tensile strength of concrete: Experimental and numerical studies. J. Mater. Civ. Eng. 32, 04020308 (2020).

    CAS 

    Google Scholar
     

  • Behnood, A., Verian, K. P. & Gharehveran, M. M. Evaluation of the splitting tensile strength in plain and steel fiber-reinforced concrete based on the compressive strength. Constr. Build. Mater. 98, 519–529 (2015).


    Google Scholar
     

  • Weerheijm, J. Understanding the tensile properties of concrete. (Elsevier, 2013).

  • Wu, Y. & Zhou, Y. Splitting tensile strength prediction of sustainable high-performance concrete using machine learning techniques. Environ. Sci. Pollut. Res. 29, 89198–89209 (2022).


    Google Scholar
     

  • Li, Q. et al. Splitting tensile strength prediction of Metakaolin concrete using machine learning techniques. Sci. Rep. 13, 20102 (2023).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yan, K., Xu, H., Shen, G. & Liu, P. Prediction of splitting tensile strength from cylinder compressive strength of concrete by support vector machine. Adv. Mater. Sci. Eng. 2013, 597257 (2013).


    Google Scholar
     

  • Zhu, Y. et al. Predicting the splitting tensile strength of recycled aggregate concrete using individual and ensemble machine learning approaches. Crystals 12, 569 (2022).


    Google Scholar
     

  • Cakiroglu, C., Aydın, Y., Bekdaş, G. & Geem, Z. W. Interpretable predictive modelling of basalt fiber reinforced concrete splitting tensile strength using ensemble machine learning methods and SHAP approach. Materials 16, 4578 (2023).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • de-Prado-Gil, J., Palencia, C., Jagadesh, P. & Martínez-García, R. A comparison of machine learning tools that model the splitting tensile strength of self-compacting recycled aggregate concrete. Materials 15, 4164 (2022).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Amin, M. N. et al. Prediction of mechanical properties of fly-ash/slag-based geopolymer concrete using ensemble and non-ensemble machine-learning techniques. Materials 15, 3478 (2022).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pakzad, S. S., Ghalehnovi, M. & Ganjifar, A. A comprehensive comparison of various machine learning algorithms used for predicting the splitting tensile strength of steel fiber-reinforced concrete. Case Study Constr. Mater. 20, e03092 (2024).


    Google Scholar
     

  • Topçu, I. B. & Sarıdemir, M. Prediction of mechanical properties of recycled aggregate concretes containing silica fume using artificial neural networks and fuzzy logic. Comput. Mater. Sci. 42, 74–82 (2008).


    Google Scholar
     

  • Wang, Q., Dai, R., Zhang, H., Zheng, H. & Liang, X. Machine learning-based prediction method for drying shrinkage of recycled aggregate concrete. J. Build. Eng. 96, 110493 (2024).

  • Beskopylny, A. N. et al. Prediction of the properties of vibro-centrifuged variatropic concrete in aggressive environments using machine learning methods. Buildings 14, 1198 (2024).


    Google Scholar
     

  • Taffese, W. Z., Wally, G. B., Magalhães, F. C. & Espinosa-Leal, L. Concrete aging factor prediction using machine learning. Mater. Today Commun. 40, 109527 (2024).

  • Liang, M. et al. Interpretable ensemble-machine-learning models for predicting creep behavior of concrete. Cem. Concr. Compos. 125, 104295 (2022).

    CAS 

    Google Scholar
     

  • Hilloulin, B., Hafidi, A., Boudache, S. & Loukili, A. Interpretable ensemble machine learning for the prediction of the expansion of cementitious materials under external sulfate attack. J. Build. Eng. 80, 107951 (2023).


    Google Scholar
     

  • Zhu, J. & Wang, Y. Convolutional neural networks for predicting creep and shrinkage of concrete. Constr. Build. Mater. 306, 124868 (2021).


    Google Scholar
     

  • Fan, Z. et al. A time-series deep learning model for predicting concrete shrinkage and creep verified with in-situ and laboratory test data. Constr. Build. Mater. 447, 138140 (2024).


    Google Scholar
     

  • Liu, K.-H., Zheng, J.-K., Pacheco-Torgal, F. & Zhao, X.-Y. Innovative modeling framework of chloride resistance of recycled aggregate concrete using ensemble-machine-learning methods. Constr. Build. Mater. 337, 127613 (2022).

    CAS 

    Google Scholar
     

  • Huang, X. et al. Frost durability prediction of rubber concrete based on improved machine learning models. Constr. Build. Mater. 429, 136201 (2024).


    Google Scholar
     

  • Cemalgil, S., Gül, E., Onat, O. & Aruntaş, H. Y. A novel prediction model for durability properties of concrete modified with steel fiber and Silica Fume by using Hybridized GRELM. Constr. Build. Mater. 341, 127856 (2022).

    CAS 

    Google Scholar
     

  • Li, H.-W. -X., Lyngdoh, G., Krishnan, N. A. & Das, S. Machine learning guided design of microencapsulated phase change materials-incorporated concretes for enhanced freeze-thaw durability. Cem. Concr. Compos. 140, 105090 (2023).

    CAS 

    Google Scholar
     

  • Li, Y. et al. Analysis and prediction of freeze-thaw resistance of concrete based on machine learning. Mater. Today Commun. 39, 108946 (2024).

    CAS 

    Google Scholar
     

  • Sun, B. et al. Enhancing concrete frost resistance prediction with an explainable neural network. Case Study Constr. Mater. 21, e03648 (2024).


    Google Scholar
     

  • Chen, H., Cao, Y., Liu, Y., Qin, Y. & Xia, L. Enhancing the durability of concrete in severely cold regions: mix proportion optimization based on machine learning. Constr. Build. Mater. 371, 130644 (2023).


    Google Scholar
     

  • Qiao, L. et al. Interpretable machine learning model for predicting freeze-thaw damage of dune sand and fiber reinforced concrete. Case Study Constr. Mater. 19, e02453 (2023).


    Google Scholar
     

  • Taheri, A. & Sobanjo, J. Ensemble learning approach for developing performance models of flexible pavement. Infrastructures 9, 78 (2024).


    Google Scholar
     

  • Zheng, W. & Cai, J. A optimum prediction model of chloride ion diffusion coefficient of machine-made sand concrete based on different machine learning methods. Constr. Build. Mater. 411, 134414 (2024).

    CAS 

    Google Scholar
     

  • Kumar, S., Kumar, D. R., Wipulanusat, W. & Keawsawasvong, S. Development of ANN-based metaheuristic models for the study of the durability characteristics of high-volume fly ash self-compacting concrete with silica fume. J. Build. Eng. 94, 109844 (2024).

  • Xie, W. et al. RACBase: a cloud-based database of recycled aggregate concrete durability. Case Study Constr. Mater. 18, e02004 (2023).


    Google Scholar
     

  • Taffese, W. Z. & Espinosa-Leal, L. Unveiling non-steady chloride migration insights through explainable machine learning. J. Build. Eng. 82, 108370 (2024).


    Google Scholar
     

  • Hosseinzadeh, M., Mousavi, S. S., Hosseinzadeh, A. & Dehestani, M. An efficient machine learning approach for predicting concrete chloride resistance using a comprehensive dataset. Sci. Rep. 13, 15024 (2023).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bamshad, O. et al. Predicting corrosion of recycled aggregate concrete under sulfuric acid rain using machine learning and uncertainty analysis. Constr. Build. Mater. 438, 137146 (2024).

    CAS 

    Google Scholar
     

  • Yu, X., Li, J., Yu, Y. & Song, A. Advancing service life estimation of reinforced concrete considering the coupling effects of multiple factors: Hybridized physical testing and machine learning approach. J. Build. Eng. 84, 108476 (2024).


    Google Scholar
     

  • Sun, Z. et al. Compressive strength resistance coefficient of sustainable concrete in sulfate environments: hybrid machine learning model and experimental verification. Mater. Today Commun. 39, 108667 (2024).

    CAS 

    Google Scholar
     

  • Tran, V. Q. Machine learning approach for investigating chloride diffusion coefficient of concrete containing supplementary cementitious materials. Constr. Build. Mater. 328, 127103 (2022).


    Google Scholar
     

  • Luo, D. & Wei, J. Efficacy of functionalized sodium-montmorillonite in mitigating alkali-silica reaction. Appl. Clay Sci. 245, 107139 (2023).

    CAS 

    Google Scholar
     

  • Luo, D., Sinha, A., Adhikari, M. & Wei, J. Mitigating alkali-silica reaction through metakaolin-based internal conditioning: new insights into property evolution and mitigation mechanism. Cem. Concr. Res. 159, 106888 (2022).

    CAS 

    Google Scholar
     

  • Luo, D. & Wei, J. Elucidating the role of magnesium in alkali-silica reaction: Performance and mechanisms. Constr. Build. Mater. 437, 136935 (2024).

    CAS 

    Google Scholar
     

  • Yang, L. et al. Prediction of alkali-silica reaction expansion of concrete using artificial neural networks. Cem. Concr. Compos. 140, 105073 (2023).

    CAS 

    Google Scholar
     

  • Allahyari, H., Heidarpour, A., Shayan, A. & Nguyen, V. P. A robust time-dependent model of alkali-silica reaction at different temperatures. Cem. Concr. Compos. 106, 103460 (2020).

    CAS 

    Google Scholar
     

  • Hariri-Ardebili, M. A. Interpretable physics-aware alkali-silica reaction expansion prediction. Constr. Build. Mater. 449, 138165 (2024).

    CAS 

    Google Scholar
     

  • Zhang, J., Zhang, Y., Marani, A. & Zhang, L. A new understanding of the alkali-silica reaction expansion in concrete using a hybrid ensemble model. J. Build. Eng. 96, 110523 (2024).


    Google Scholar
     

  • Yang, Y. et al. Evaluation of multiple machine learning models for ASR expansion of concrete. Mater. Today Commun. 39, 109045 (2024).

    CAS 

    Google Scholar
     

  • Ai, L., Soltangharaei, V. & Ziehl, P. Developing a heterogeneous ensemble learning framework to evaluate Alkali-silica reaction damage in concrete using acoustic emission signals. Mech. Syst. Signal Process. 172, 108981 (2022).


    Google Scholar
     

  • Ali, F., Nadjai, A., Silcock, G. & Abu-Tair, A. Outcomes of a major research on fire resistance of concrete columns. Fire Saf. J. 39, 433–445 (2004).

    CAS 

    Google Scholar
     

  • Li, S., Liew, J. R. & Xiong, M.-X. Prediction of fire resistance of concrete encased steel composite columns using artificial neural network. Eng. Struct. 245, 112877 (2021).


    Google Scholar
     

  • Zhao, X.-Y., Chen, J.-X. & Wu, B. An interpretable ensemble-learning-based open source model for evaluating the fire resistance of concrete-filled steel tubular columns. Eng. Struct. 270, 114886 (2022).


    Google Scholar
     

  • Naser, M. & Kodur, V. Explainable machine learning using real, synthetic and augmented fire tests to predict fire resistance and spalling of RC columns. Eng. Struct. 253, 113824 (2022).


    Google Scholar
     

  • Sirisena, G. et al. Machine learning-based framework for predicting the fire-induced spalling in concrete tunnel linings. Tunn. Undergr. Space Technol. 153, 106000 (2024).


    Google Scholar
     

  • Chen, H., Yang, J. & Chen, X. A convolution-based deep learning approach for estimating compressive strength of fiber reinforced concrete at elevated temperatures. Constr. Build. Mater. 313, 125437 (2021).


    Google Scholar
     

  • Cook, R. et al. Machine learning for high-fidelity prediction of cement hydration kinetics in blended systems. Mater. Des. 208, 109920 (2021).

    CAS 

    Google Scholar
     

  • Lapeyre, J. et al. Machine learning enables prompt prediction of hydration kinetics of multicomponent cementitious systems. Sci. Rep. 11, 3922 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Han, T. et al. Deep learning to predict the hydration and performance of fly ash-containing cementitious binders. Cem. Concr. Res. 165, 107093 (2023).

    CAS 

    Google Scholar
     

  • Peng, Y. & Unluer, C. Interpretable machine learning-based analysis of hydration and carbonation of carbonated reactive magnesia cement mixes. J. Clean. Prod. 434, 140054 (2024).

    CAS 

    Google Scholar
     

  • Scrivener, K. L. Backscattered electron imaging of cementitious microstructures: understanding and quantification. Cem. Concr. Compos. 26, 935–945 (2004).

    CAS 

    Google Scholar
     

  • Sheiati, S., Nguyen, H., Kinnunen, P. & Ranjbar, N. Cementitious phase quantification using deep learning. Cem. Concr. Res. 172, 107231 (2023).

    CAS 

    Google Scholar
     

  • Yu, Y. & Geng, G. Deep learning methods for phase segmentation in backscattered electron images of cement paste and SCM-blended systems. Cem. Concr. Compos. 155, 105810 (2025).

    CAS 

    Google Scholar
     

  • Chao, Z. et al. Permeability and porosity of light-weight concrete with plastic waste aggregate: Experimental study and machine learning modelling. Constr. Build. Mater. 411, 134465 (2024).


    Google Scholar
     

  • Xu, W. et al. AI-infused characteristics prediction and multi-objective design of ultra-high performance concrete (UHPC): from pore structures to macro-performance. J. Build. Eng. 98, 111170 (2024).


    Google Scholar
     

  • Zhang, L. et al. Understanding and predicting micro-characteristics of ultra-high performance concrete (UHPC) with green porous lightweight aggregates: insights from machine learning techniques. Constr. Build. Mater. 446, 138021 (2024).

    CAS 

    Google Scholar
     

  • Xi, X., Yin, Z., Yang, S. & Li, C.-Q. Using artificial neural network to predict the fracture properties of the interfacial transition zone of concrete at the meso-scale. Eng. Fract. Mech. 242, 107488 (2021).


    Google Scholar
     

  • Jamali, A. et al. Novel multi-scale experimental approach and deep learning model to optimize capillary pressure evolution in early age concrete. Cem. Concr. Res. 180, 107490 (2024).

    CAS 

    Google Scholar
     

  • Chen, X., Sun, T., Sun, T., Yin, H. & Hou, D. Curve clustering analysis and intelligent recognition of grid nanoindentation data for cementitious material. Constr. Build. Mater. 425, 135732 (2024).


    Google Scholar
     

  • Arachchige, R. M., Olek, J., Rajabipour, F. & Peethamparan, S. Phase identification and micromechanical properties of non-traditional and natural pozzolan based alkali-activated materials. Constr. Build. Mater. 441, 137478 (2024).

    CAS 

    Google Scholar
     

  • Mir, B. et al. Machine learning-based evaluation of the damage caused by cracks on concrete structures. Precis. Eng. 76, 314–327 (2022).


    Google Scholar
     

  • Yang, L. et al. Automated wall-climbing robot for concrete construction inspection. J. Field Robot. 40, 110–129 (2023).


    Google Scholar
     

  • Bae, H. & An, Y.-K. Computer vision-based statistical crack quantification for concrete structures. Measurement 211, 112632 (2023).


    Google Scholar
     

  • Ren, Q. et al. Automatic quality compliance checking in concrete dam construction: Integrating rule syntax parsing and semantic distance. Adv. Eng. Inform. 60, 102409 (2024).


    Google Scholar
     

  • Adeli, H. Expert systems in construction and structural engineering. (CRC Press, 1988).

  • Ma, Z., Liu, Y. & Li, J. Review on automated quality inspection of precast concrete components. Autom. Constr. 150, 104828 (2023).


    Google Scholar
     

  • Kardovskyi, Y. & Moon, S. Artificial intelligence quality inspection of steel bars installation by integrating mask R-CNN and stereo vision. Autom. Constr. 130, 103850 (2021).


    Google Scholar
     

  • Pellegrino, C., Faleschini, F. & Meyer, C. Recycled materials in concrete. Developments in the Formulation and Reinforcement of Concrete, 19-54 (2019).

  • Nguyen, T.-D. et al. Artificial intelligence algorithms for prediction and sensitivity analysis of mechanical properties of recycled aggregate concrete: a review. J. Build. Eng. 66, 105929 (2023).


    Google Scholar
     

  • Dong, C. et al. Fresh and hardened properties of recycled plastic fiber reinforced self-compacting concrete made with recycled concrete aggregate and fly ash, slag, silica fume. J. Build. Eng. 62, 105384 (2022).


    Google Scholar
     

  • Duan, Z.-H., Kou, S.-C. & Poon, C.-S. Prediction of compressive strength of recycled aggregate concrete using artificial neural networks. Constr. Build. Mater. 40, 1200–1206 (2013).


    Google Scholar
     

  • Zhao, H. et al. Triaxial compressive performance of recycled aggregate/glass sand concrete: experimental study and mechanism analysis. J. Clean. Prod. 442, 141006 (2024).


    Google Scholar
     

  • Feng, J. et al. Efficient creep prediction of recycled aggregate concrete via machine learning algorithms. Constr. Build. Mater. 360, 129497 (2022).


    Google Scholar
     

  • Peng, Y. & Unluer, C. Modeling the mechanical properties of recycled aggregate concrete using hybrid machine learning algorithms. Resour. Conserv. Recycl.190, 106812 (2023).

    CAS 

    Google Scholar
     

  • Han, T., Siddique, A., Khayat, K., Huang, J. & Kumar, A. An ensemble machine learning approach for prediction and optimization of modulus of elasticity of recycled aggregate concrete. Constr. Build. Mater. 244, 118271 (2020).

    CAS 

    Google Scholar
     

  • Pal, A., Ahmed, K. S., Hossain, F. Z. & Alam, M. S. Machine learning models for predicting compressive strength of fiber-reinforced concrete containing waste rubber and recycled aggregate. J. Clean. Prod. 423, 138673 (2023).

    CAS 

    Google Scholar
     

  • Raffoul, S. et al. Behaviour of unconfined and FRP-confined rubberised concrete in axial compression. Constr. Build. Mater. 147, 388–397 (2017).

    CAS 

    Google Scholar
     

  • Ly, H.-B., Nguyen, T.-A. & Tran, V. Q. Development of deep neural network model to predict the compressive strength of rubber concrete. Constr. Build. Mater. 301, 124081 (2021).


    Google Scholar
     

  • Gao, X., Yang, J., Zhu, H. & Xu, J. Estimation of rubberized concrete frost resistance using machine learning techniques. Constr. Build. Mater. 371, 130778 (2023).


    Google Scholar
     

  • Amin, M. N. et al. Experimental and machine learning approaches to investigate the effect of waste glass powder on the flexural strength of cement mortar. PloS one 18, e0280761 (2023).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ben Seghier, M. E. A., Golafshani, E. M., Jafari-Asl, J. & Arashpour, M. Metaheuristic-based machine learning modeling of the compressive strength of concrete containing waste glass. Struct. Concr. 24, 5417–5440 (2023).


    Google Scholar
     

  • Sobuz, M. H. R. et al. Assessment of mechanical properties with machine learning modeling and durability, and microstructural characteristics of a biochar-cement mortar composite. Constr. Build. Mater. 411, 134281 (2024).


    Google Scholar
     

  • Nilimaa, J. Smart materials and technologies for sustainable concrete construction. Dev. Built Environ. 15, 100177 (2023).


    Google Scholar
     

  • Ramadan Suleiman, A. & Nehdi, M. L. Modeling self-healing of concrete using hybrid genetic algorithm–artificial neural network. Materials 10, 135 (2017).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nazar, S. et al. Development of the new prediction models for the compressive strength of nanomodified concrete using novel machine learning techniques. Buildings 12, 2160 (2022).


    Google Scholar
     

  • Sowjanya, G., Anadinni, S. & Mahadevaiah, T. Application and validation of internally cured concrete strength characteristics by machine learning. Mater. Today Proc. (2023).

  • De Belie, N. et al. A review of self-healing concrete for damage management of structures. Adv. Mater. interfaces 5, 1800074 (2018).


    Google Scholar
     

  • Sinha, A., Lim, D. Z. H. & Wei, J. A lignin-based capsule system with tunable properties tailored for robust self-healing concrete. Cem. Concr. Compos. 132, 104643 (2022).

    CAS 

    Google Scholar
     

  • Wang, W., Moreau, N. G., Yuan, Y., Race, P. R. & Pang, W. Towards machine learning approaches for predicting the self-healing efficiency of materials. Comput. Mater. Sci. 168, 180–187 (2019).


    Google Scholar
     

  • Zhuang, X. & Zhou, S. The prediction of self-healing capacity of bacteria-based concrete using machine learning approaches. Comput. Mater. Continua 59, 57–77 (2019).

  • Theja, A. R., Reddy, M. S., Jindal, B. B. & Sashidhar, C. Predicting the strength properties of self healing concrete using artificial neural network. J. Soft Comput. Civ. Eng. 7, 56–71 (2023).


    Google Scholar
     

  • Xi, B., Huang, Z., Al-Obaidi, S. & Ferrara, L. Predicting ultra high-performance concrete self-healing performance using hybrid models based on metaheuristic optimization techniques. Constr. Build. Mater. 381, 131261 (2023).


    Google Scholar
     

  • Ashwini, R. et al. Compressive and flexural strength of concrete with different nanomaterials: a critical review. J. Nanomater.2023, 1004597 (2023).


    Google Scholar
     

  • Allujami, H. M. et al. Nanomaterials in recycled aggregates concrete applications: Mechanical properties and durability. A review. Cogent Eng. 9, 2122885 (2022).


    Google Scholar
     

  • Onaizi, A. M., Huseien, G. F., Lim, N. H. A. S., Amran, M. & Samadi, M. Effect of nanomaterials inclusion on sustainability of cement-based concretes: a comprehensive review. Constr. Build. Mater. 306, 124850 (2021).

    CAS 

    Google Scholar
     

  • Li, X. Application of artificial intelligence technologies in concrete and nanomaterials. Highlights Sci., Eng. Technol. 75, 246–250 (2023).


    Google Scholar
     

  • Zeyad, A. M. et al. Compressive strength of nano concrete materials under elevated temperatures using machine learning. Sci. Rep. 14, 24246 (2024).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Maherian, M. F., Baran, S., Bicakci, S. N., Toreyin, B. U. & Atahan, H. N. Machine learning-based compressive strength estimation in nano silica-modified concrete. Constr. Build. Mater. 408, 133684 (2023).

    CAS 

    Google Scholar
     

  • Dong, W., Huang, Y., Lehane, B. & Ma, G. Multi-objective design optimization for graphite-based nanomaterials reinforced cementitious composites: A data-driven method with machine learning and NSGA-II. Constr. Build. Mater. 331, 127198 (2022).

    CAS 

    Google Scholar
     

  • Hosseinzadeh, M., Mousavi, S. S. & Dehestani, M. An ensemble learning-based prediction model for the compressive strength degradation of concrete containing superabsorbent polymers (SAP). Sci. Rep. 14, 18535 (2024).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Das, N. Advantages and disadvantages of Expert Systems. (ilearnlot, 2018).

  • Liao, S.-H. Expert system methodologies and applications—a decade review from 1995 to 2004. Expert Syst. Appl. 28, 93–103 (2005).


    Google Scholar
     

  • Pezeshki, Z. & Mazinani, S. M. Comparison of artificial neural networks, fuzzy logic and neuro fuzzy for predicting optimization of building thermal consumption: a survey. Artif. Intell. Rev. 52, 495–525 (2019).


    Google Scholar
     

  • Johnson, J. A. & Smartt, H. B. Advantages of an alternative form of fuzzy logic. IEEE Trans. fuzzy Syst. 3, 149–157 (1995).


    Google Scholar
     

  • Stopponi, S., Pedrazzini, N., Peels-Matthey, S., McGillivray, B. & Nissim, M. Natural Language Processing for Ancient Greek: design, advantages and challenges of language models. Diachronica (2024).

  • Wu, C. et al. Natural language processing for smart construction: Current status and future directions. Autom. Constr. 134, 104059 (2022).


    Google Scholar
     

  • Llale, J. et al. in The Construction Industry in the Fourth Industrial Revolution: Proceedings of 11th Construction Industry Development Board (CIDB) Postgraduate Research Conference 11. 197-204 (Springer).

  • Koch, C., Georgieva, K., Kasireddy, V., Akinci, B. & Fieguth, P. A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Adv. Eng. Inform. 29, 196–210 (2015).


    Google Scholar
     

  • Zhang, Q., Barri, K., Jiao, P., Salehi, H. & Alavi, A. H. Genetic programming in civil engineering: advent, applications and future trends. Artif. Intell. Rev. 54, 1863–1885 (2021).


    Google Scholar
     

  • Kudus, S. A. et al. An overview current application of artificial neural network in concrete. Adv. Mater. Res. 626, 372–375 (2013).


    Google Scholar
     

  • Parisi, F. et al. A new concept for large additive manufacturing in construction: tower crane-based 3D printing controlled by deep reinforcement learning. Constr. Innov. 24, 8–32 (2024).


    Google Scholar
     

  • Mijwil, M. M., Hiran, K. K., Doshi, R. & Unogwu, O. J. Advancing construction with IoT and RFID technology in civil engineering: A technology review. Al-Salam J. Eng. Technol. 2, 54–62 (2023).


    Google Scholar
     

  • Gordan, M. et al. State-of-the-art review on advancements of data mining in structural health monitoring. Measurement 193, 110939 (2022).


    Google Scholar
     

  • Chou, J.-S., Chiu, C.-K., Farfoura, M. & Al-Taharwa, I. Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques. J. Comput. Civ. Eng. 25, 242–253 (2011).


    Google Scholar
     

  • Xiong, W., Wang, Y., Wu, J., Hu, Z. & Li, B. Concrete slump prediction based on hybrid optimization XGBoost algorithm. Adv. Comput. Signals Syst. 7, 74–83 (2023).


    Google Scholar
     

  • Cakiroglu, C., Bekdaş, G., Kim, S. & Geem, Z. W. Explainable ensemble learning models for the rheological properties of self-compacting concrete. Sustainability 14, 14640 (2022).

    CAS 

    Google Scholar
     

  • Gao, H. et al. Rheological behavior of 3D printed concrete: Influential factors and printability prediction scheme. J. Build. Eng. 91, 109626 (2024).


    Google Scholar
     

  • el Mahdi Safhi, A., Dabiri, H., Soliman, A. & Khayat, K. H. Prediction of self-consolidating concrete properties using XGBoost machine learning algorithm: Rheological properties. Powder Technol. 438, 119623 (2024).


    Google Scholar
     

  • Cheng, B. et al. AI-guided proportioning and evaluating of self-compacting concrete based on rheological approach. Constr. Build. Mater. 399, 132522 (2023).


    Google Scholar
     

  • Amin, M. N. et al. Predicting the rheological properties of super-plasticized concrete using modeling techniques. Materials 15, 5208 (2022).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, P., Gao, Z., Wang, J., Guo, J. & Wang, T. Influencing factors analysis and optimized prediction model for rheology and flowability of nano-SiO2 and PVA fiber reinforced alkali-activated composites. J. Clean. Prod. 366, 132988 (2022).

    CAS 

    Google Scholar
     

  • Nazar, S. et al. Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymer. J. Mater. Res. Technol. 24, 100–124 (2023).


    Google Scholar
     

  • Zou, B. et al. Artificial intelligence-based optimized models for predicting the slump and compressive strength of sustainable alkali-derived concrete. Constr. Build. Mater. 409, 134092 (2023).

    CAS 

    Google Scholar
     

  • Rehman, F., Khokhar, S. A. & Khushnood, R. A. ANN based predictive mimicker for mechanical and rheological properties of eco-friendly geopolymer concrete. Case Study Constr. Mater. 17, e01536 (2022).


    Google Scholar
     

  • Prasad, B. R., Eskandari, H. & Reddy, B. V. Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN. Constr. Build. Mater. 23, 117–128 (2009).


    Google Scholar
     

  • Aicha, M. B., Al Asri, Y., Zaher, M., Alaoui, A. H. & Burtschell, Y. Prediction of rheological behavior of self-compacting concrete by multi-variable regression and artificial neural networks. Powder Technol. 401, 117345 (2022).


    Google Scholar
     

  • Hilloulin, B. & Tran, V. Q. Interpretable machine learning model for autogenous shrinkage prediction of low-carbon cementitious materials. Constr. Build. Mater. 396, 132343 (2023).

    CAS 

    Google Scholar
     

  • Minfei, L. et al. Microstructure-informed deep convolutional neural network for predicting short-term creep modulus of cement paste. Cem. Concr. Res. 152, 106681 (2022).

    CAS 

    Google Scholar
     

  • Kiani, B., Sajedi, S., Gandomi, A. H., Huang, Q. & Liang, R. Y. Optimal adjustment of ACI formula for shrinkage of concrete containing pozzolans. Constr. Build. Mater. 131, 485–495 (2017).

    CAS 

    Google Scholar
     

  • Ghasemzadeh, F., Manafpour, A., Sajedi, S., Shekarchi, M. & Hatami, M. Predicting long-term compressive creep of concrete using inverse analysis method. Constr. Build. Mater. 124, 496–507 (2016).


    Google Scholar
     

  • Cao, J. et al. Prediction models for creep and creep recovery of fly ash concrete. Constr. Build. Mater. 428, 136398 (2024).


    Google Scholar
     

  • Yang, Z., Zhu, H., Zhang, B., Dong, Z. & Wu, P. Short-term creep behaviors of seawater sea-sand coral aggregate concrete: an experimental study with Rheological model and neural network. Constr. Build. Mater. 363, 129786 (2023).


    Google Scholar
     

  • Hilloulin, B. & Tran, V. Q. Using machine learning techniques for predicting autogenous shrinkage of concrete incorporating superabsorbent polymers and supplementary cementitious materials. J. Build. Eng. 49, 104086 (2022).


    Google Scholar
     

  • Liu, K., Zou, C., Zhang, X. & Yan, J. Innovative prediction models for the frost durability of recycled aggregate concrete using soft computing methods. J. Build. Eng. 34, 101822 (2021).


    Google Scholar
     

  • Chen, B. et al. Optimization of high-performance concrete mix ratio design using machine learning. Eng. Appl. Artif. Intell. 122, 106047 (2023).


    Google Scholar
     

  • Atasham ul haq, M., Xu, W., Abid, M. & Gong, F. Prediction of progressive frost damage development of concrete using machine-learning algorithms. Buildings 13, 2451 (2023).


    Google Scholar
     

  • Hafez, H., Teirelbar, A., Kurda, R., Tošić, N. & de la Fuente, A. Pre-bcc: A novel integrated machine learning framework for predicting mechanical and durability properties of blended cement concrete. Constr. Build. Mater. 352, 129019 (2022).

    CAS 

    Google Scholar
     

  • Taffese, W. Z. & Espinosa-Leal, L. Prediction of chloride resistance level of concrete using machine learning for durability and service life assessment of building structures. J. Build. Eng. 60, 105146 (2022).


    Google Scholar
     

  • Zhang, H. et al. Analyzing chloride diffusion for durability predictions of concrete using contemporary machine learning strategies. Mater. Today Commun. 38, 108543 (2024).

    CAS 

    Google Scholar
     

  • Liu, W., Liu, G. & Zhu, X. Applicability of machine learning algorithms in predicting chloride diffusion in concrete: Modeling, evaluation, and feature analysis. Case Study Constr. Mater. 21, e03573 (2024).


    Google Scholar
     

  • Huang, X. et al. Chloride permeability coefficient prediction of rubber concrete based on the improved machine learning technical: modelling and performance evaluation. Polymers 15, 308 (2023).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yoon, Y.-S. et al. Evaluation of durability performance for chloride ingress considering long-term aged GGBFS and FA concrete and analysis of the relationship between concrete mixture characteristic and passed charge using machine learning algorithm. Materials 16, 7459 (2023).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lv, W. et al. Hybrid machine learning-based model for predicting chloride ion concentration in coral aggregate concrete and its ethically aligned graphical user interface design. Mater. Today Commun. 37, 107053 (2023).

    CAS 

    Google Scholar
     

  • Li, L. et al. Prediction and prevention of concrete chloride penetration: machine learning and MICP techniques. Front. Mater. 11, 1445547 (2024).


    Google Scholar
     

  • Al Fuhaid, A. F. & Alanazi, H. Prediction of chloride diffusion coefficient in concrete modified with supplementary cementitious materials using machine learning algorithms. Materials 16, 1277 (2023).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sun, Z., Li, Y., Li, Y., Su, L. & He, W. Prediction of chloride ion concentration distribution in basalt-polypropylene fiber reinforced concrete based on optimized machine learning algorithm. Mater. Today Commun. 36, 106565 (2023).

    CAS 

    Google Scholar
     

  • Cai, R. et al. Prediction of surface chloride concentration of marine concrete using ensemble machine learning. Cem. Concr. Res. 136, 106164 (2020).

    CAS 

    Google Scholar
     

  • Haizhen, H. et al. Similarity analysis of chloride transport behavior in fly ash concrete under different environments aiding by machine learning method. Case Study Constr. Mater. 20, e03270 (2024).


    Google Scholar
     

  • Ahmad, A., Farooq, F., Ostrowski, K. A., Śliwa-Wieczorek, K. & Czarnecki, S. Application of novel machine learning techniques for predicting the surface chloride concentration in concrete containing waste material. Materials 14, 2297 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Luo, D., Zhou, M., Li, F. & Niu, D. Chloride ion transport in coral aggregate concrete subjected to coupled erosion by sulfate and chloride salts in drying-wetting cycles. J. Mater. Res. Technol. 30, 3251–3267 (2024).

    CAS 

    Google Scholar
     

  • Wang, R. et al. Machine learning method to explore the correlation between fly ash content and chloride resistance. Materials 17, 1192 (2024).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ofuyatan, O. M., Muhit, I. B., Babafemi, A. J. & Osibanjo, I. in Structures. 105423 (Elsevier).

  • Cao, K. et al. Failure node prediction study of in-service tunnel concrete for sulfate attack by PSO-LSTM based on Markov correction. Case Study Constr. Mater. 20, e03153 (2024).


    Google Scholar
     



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