Advancements in Machine Learning for the Development of Cementitious Composites Toward an Intelligent and Green Lifecycle: A State-of-the-Art Review

Jinyang Jiang; Junlin Lin; Lin Jin; Fengjuan Wang; Zhiyong Liu; Yingze Li; Zeyu Lu

doi:10.1016/j.eng.2026.02.012

Engineering ›› :202602012 DOI: 10.1016/j.eng.2026.02.012

Research

research-article

Advancements in Machine Learning for the Development of Cementitious Composites Toward an Intelligent and Green Lifecycle: A State-of-the-Art Review

Author information +

History +

PDF (7368KB)

Abstract

As fundamental construction materials, cementitious composites face significant challenges under conventional development approaches, including carbon-intensive production, resource-intensive experimentation, and inefficient design processes. With the emergence of machine learning (ML) as a transformative solution to these limitations, this study presents a state-of-the-art review of existing research to highlight its potential in advancing the development of cementitious composites with intelligent and green lifecycles. The review first provides a foundational introduction to ML concepts and then proposes a novel four-quadrant classification framework to systematically organize current ML applications in the field. The ML-driven innovations integrate the component-structure-process-performance relationships of cementitious composites through sustainable material selection, effective characterization, accurate performance prediction, and optimized inverse design, collectively promoting a paradigm shift toward intelligent and green lifecycles. Furthermore, critical implementation challenges are examined across technical, methodological, and operational dimensions, together with corresponding solution strategies. This review ultimately offers both a conceptual framework and practical implementation guidelines for the development of next-generation sustainable construction materials.

Keywords

Machine learning / Cementitious composites / Intelligent / Green / Lifecycle

Cite this article

Download citation ▾

Jinyang Jiang, Junlin Lin, Lin Jin, Fengjuan Wang, Zhiyong Liu, Yingze Li, Zeyu Lu. Advancements in Machine Learning for the Development of Cementitious Composites Toward an Intelligent and Green Lifecycle: A State-of-the-Art Review. Engineering 202602012 DOI:10.1016/j.eng.2026.02.012

登录浏览全文

4963

注册一个新账户忘记密码

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Schmidt W, Alexander M, John V. Education for sustainable use of cement based materials. Cem Concr Res 2018; 114:103-14.

[2]	Shi C, Qu B, Provis JL. Recent progress in low-carbon binders. Cem Concr Res 2019; 122:227-50.

[3]	Felipe Arbeláez Pérez O, Senior Arrieta V, Hernán Gómez Ospina J, Herrera Herrera S, Ferney Rodríguez Rojas C, María Santis Navarro A. Carbon dioxide emissions from traditional and modified concrete. A review. Environ Dev 2024; 52:101036.

[4]	Cheng D, Reiner DM, Yang F, Cui C, Meng J, Shan Y, et al. Projecting future carbon emissions from cement production in developing countries. Nat Commun 2023; 14(1):8213.

[5]	Monteiro PJM, Miller SA, Horvath A. Towards sustainable concrete. Nat Mater 2017; 16(7):698-9.

[6]	Hanifa M, Agarwal R, Sharma U, Thapliyal PC, Singh LP. A review on CO₂ capture and sequestration in the construction industry: emerging approaches and commercialised technologies. J CO₂ Util 2023; 67:102292.

[7]	Leslie M. Construction industry innovation takes aim at reducing carbon emissions. Engineering 2022; 19:7-10.

[8]	Provis JL, Bernal SA, Zhang Z. The decarbonization of construction—how can alkali-activated materials contribute? Engineering 2024; 37:18-21.

[9]	Barbhuiya S, Kanavaris F, Das BB, Idrees M. Decarbonising cement and concrete production: strategies, challenges and pathways for sustainable development. J Build Eng 2024; 86:108861.

[10]	Coffetti D, Crotti E, Gazzaniga G, Carrara M, Pastore T, Coppola L. Pathways towards sustainable concrete. Cem Concr Res 2022; 154:106718.

[11]	He P, Drissi S, Hu X, Liu J, Shi C. Properties of CO₂-cured cement incorporating fly ash and slag subjected to further water curing. Cem Concr Compos 2024; 152:105633.

[12]	Beskopylny AN, Stel’Makh SA, Shcherban’ EM, Saidumov MS, Abumuslimov A, Mezhidov D, et al. Eco-friendly foam concrete with improved physical and mechanical properties, modified with fly ash and reinforced with coconut fibers. Constr Mater. Prod 2025; 8(1):1.

[13]	Bespolitov DV, Konovalova NA, Dabizha ON, Pankov PP, Rush EA. Influence of the mechanical activation of fly ash on strength of ground concrete based on waste production. Ecol Ind Russ 2021; 25(11):36-41.

[14]	Kharun M, Klyuev S, Koroteev D, Chiadighikaobi PC, Fediuk R, Olisov A, et al. Heat treatment of basalt fiber reinforced expanded clay concrete with increased strength for cast-in-situ construction. Fibers 2020; 8(11):67.

[15]	Scrivener K, Martirena F, Bishnoi S, Maity S. Calcined clay limestone cements (LC³). Cem Concr Res 2018; 114:49-56.

[16]	Tanhadoust A, Emadi SAA, Nasrollahpour S, Dabbaghi F, Nehdi ML. Optimal design of sustainable recycled rubber-filled concrete using life cycle assessment and multi-objective optimization. Constr Build Mater 2023; 402:132878.

[17]	Yu Y, Fang G, Kurda R, Sabuj AR, Zhao XY. An agile, intelligent and scalable framework for mix design optimization of green concrete incorporating recycled aggregates from precast rejects. Case Stud Constr Mater 2024; 20:e03156.

[18]	Pfeiffer OP, Gong K, Severson KA, Chen J, Gregory JR, Ghosh S, et al. Bayesian design of concrete with amortized Gaussian processes and multi-objective optimization. Cem Concr Res 2024; 177:107406.

[19]	Van Damme H. Concrete material science: past, present, and future innovations. Cem Concr Res 2018; 112:5-24.

[20]	Raabe D, Mianroodi JR, Neugebauer J. Accelerating the design of compositionally complex materials via physics-informed artificial intelligence. Nat Comput Sci 2023; 3(3):198-209.

[21]	Malik S, Muhammad K, Waheed Y. Artificial intelligence and industrial applications-a revolution in modern industries. Ain Shams Eng J 2024; 15(9):102886.

[22]	Gallegos M, Vassilev-Galindo V, Poltavsky I, Martín Pendás Á, Tkatchenko A. Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors. Nat Commun 2024; 15(1):4345.

[23]	Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science 2015; 349(6245):255-60.

[24]	Vityaev EE, Goncharov SS, Sviridenko DI. Task-driven approach to artificial intelligence. Cogn Syst Res 2023; 81:50-6.

[25]	Wu H, Zhang J, Bao Z, Wang G, Wang W, Yang Y, et al. Runoff modeling in ungauged catchments using machine learning algorithm-based model parameters regionalization methodology. Engineering 2023; 28:93-104.

[26]	Zheng X, Li P, Wu X. Data stream classification based on extreme learning machine: a review. Big Data Res 2022; 30:100356.

[27]	Chandwani V, Agrawal V, Nagar R. Modeling slump of ready mix concrete using genetic algorithms assisted training of artificial neural networks. Expert Syst Appl 2015; 42(2):885-93.

[28]	Jiang JY, Liu Y, Lin JL, Yang T, Wang F. Machine learning assisted design of low-carbon aluminosilicate cementitious composites with diverse raw materials and target mechanical strength. Case Stud Constr Mater 2025; 22:e04664.

[29]	Li Y, Liu Y, Lin H, Jin C. Study of flexural strength of concrete containing mineral admixtures based on machine learning. Sci Rep 2023; 13(1):18061.

[30]	Dai J, Yang X, He J, Wang Q, Zhang Z. Machine learning prediction of electric flux in concrete and mix proportion optimization design. Mater Today Commun 2024; 38:107778.

[31]	Li WH, Xiong CC, Zhou Y, Chen W, Zheng Y, Lin W, et al. Insights on the mechanical properties and failure mechanisms of calcium silicate hydrates based on deep-learning potential molecular dynamics. Cem Concr Res 2024; 186:107690.

[32]	Wu ZY, Pan H, Huang P, Tang J, She W. Biomimetic mechanical robust cement-resin composites with machine learning-assisted gradient hierarchical structures. Adv Mater 2024; 36(35):2405183.

[33]	Golafshani EM, Behnood A, Kim T, Ngo T, Kashani A. A framework for low-carbon mix design of recycled aggregate concrete with supplementary cementitious materials using machine learning and optimization algorithms. Structures 2024; 61:106143.

[34]	Butakov EB, Abdurakipov SS, Neznamov VY, Alekseenko SV. On Increasing the efficiency of a cement clinker kiln using machine learning. J Eng Thermophys 2024; 33(4):675-82.

[35]	Zheng W, Liu H, Zhou XY, Xue XJ, Li H, Liu JX. A soft sensor model based on an improved semi-supervised stacked autoencoder for just-in-time updating of cement clinker production process data f-CaO. Meas Sci Technol 2024; 35(5):056203.

[36]	Li H, Lyngdoh G, Krishnan NMA, Das S. Machine learning guided design of microencapsulated phase change materials-incorporated concretes for enhanced freeze-thaw durability. Cem Concr Compos 2023; 140:105090.

[37]	Elshaarawy MK, Alsaadawi MM, Hamed AK. Machine learning and interactive GUI for concrete compressive strength prediction. Sci Rep 2024; 14(1):16694.

[38]	Jin L, Duan J, Jin Y, Xue P, Zhou P. Prediction of HPC compressive strength based on machine learning. Sci Rep 2024; 14(1):16776.

[39]	Das AK, Suthar D, Leung CKY. Machine learning based crack mode classification from unlabeled acoustic emission waveform features. Cem Concr Res 2019; 121:42-57.

[40]	Aravind N, Nagajothi S, Elavenil S. Machine learning model for predicting the crack detection and pattern recognition of geopolymer concrete beams. Constr Build Mater 2021; 297:123785.

[41]	Althoey F, Amin MN, Khan K, Usman MM, Khan MA, Javed MF, et al. Machine learning based computational approach for crack width detection of self-healing concrete. Case Stud Constr Mater 2022; 17:e01610.

[42]	Albert C, Isgor OB, Angst U. Exploring machine learning to predict the pore solution composition of hardened cementitious systems. Cem Concr Res 2022; 162:107001.

[43]	Vedrtnam A, Kumar S, Barluenga G, Chaturvedi S. Early crack detection using modified spectral clustering method assisted with FE analysis for distress anticipation in cement-based composites. Sci Rep 2021; 11(1):19685.

[44]	Jia H, Qiao G, Han P. Machine learning algorithms in the environmental corrosion evaluation of reinforced concrete structures-a review. Cem Concr Compos 2022; 133:104725.

[45]	Taffese WZ, Wally GB, Magalhães FC, Espinosa-Leal L. Concrete aging factor prediction using machine learning. Mater Today Commun 2024; 40:109527.

[46]	Qayyum A, Javed MF, Asghar R, Iqtidar A, Alabduljabbar H, Khan MA, et al. Promoting the sustainable construction: a scientometric review on the utilization of waste glass in concrete. Rev Adv Mater Sci 2024; 63(1):20240036.

[47]	Bodade V, Kadrolli V. Machine learning based energy efficiency analysis with concrete waste reduction techniques and carbon footprint modelling. Adv Concr Constr 2024; 18(2):135-46.

[48]	Rambabu GV, Swetha RR, Parwekar P, Jangir P, Amutha S, Sivaramaraju VV. Energy efficiency and concrete waste management based on machine learning in sustainable construction. Adv Concr Constr 2024; 18(2):125-33.

[49]	Huang Z, Zhou H, Miao Z, Tang H, Lin B, Zhuang W. Life-Cycle Carbon Emissions (LCCE) of buildings: implications, calculations, and reductions. Engineering 2024; 35:115-39.

[50]	Ni BW, Rahman MZ, Guo SC, Zhu D. A review on properties and multi-objective performance predictions of concrete based on machine learning models. Mater Today Commun 2025; 44:112017.

[51]	Li FY, Rana MS, Qurashi MA. Advanced machine learning techniques for predicting concrete mechanical properties: a comprehensive review of models and methodologies. Multiscale Multidiscip Model Exp Des 2025; 8:110.

[52]	Moein MM, Saradar A, Rahmati K, Mousavinejad SHG, Bristow J, Aramali V, et al. Predictive models for concrete properties using machine learning and deep learning approaches: a review. J Build Eng 2023; 63(Pt A):105444.

[53]	Dinesh A, Prasad R. Predictive models in machine learning for strength and life cycle assessment of concrete structures. Autom Constr 2024; 162:105412.

[54]	Gamil Y. Machine learning in concrete technology: a review of current researches, trends, and applications. Front Built Environ 2023; 9:1145591.

[55]	Nilsson NJ. Principles of artificial intelligence. Berlin: Springer Science & Business Media; 1982.

[56]	LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521(7553):436-44.

[57]	Lei N, An D, Guo Y, Su K, Liu S, Luo Z, et al. A geometric understanding of deep learning. Engineering 2020; 6(3):361-74.

[58]	Patil DJ, Mason H. Data driven. Sebastopol: O’Reilly Media, Inc.; 2015.

[59]	Zhou L, Pan S, Wang J, Vasilakos AV. Machine learning on big data: opportunities and challenges. Neurocomputing 2017; 237:350-61.

[60]	Wang H, Wang Y, Wang X, Yin WX, Yu TC, Xue CH, et al. Multimodal machine learning guides low carbon aeration strategies in urban wastewater treatment. Engineering 2024; 36:51-62.

[61]	Zhou Y, Meng S, Lou Y, Kong Q. Physics-informed deep learning-based real-time structural response prediction method. Engineering 2024; 35:140-57.

[62]	Zhou T, Song Z, Sundmacher K. Big data creates new opportunities for materials research: a review on methods and applications of machine learning for materials design. Engineering 2019; 5(6):1017-26.

[63]	Mahesh B. Machine learning algorithms-a review. Int J Sci Res 2020; 9(1):381-6.

[64]	Tan J, Yang J, Wu S, Chen G, Zhao J. A critical look at the current train/test split in machine learning. arXiv:2106.04525.

[65]	Nti IK, Nyarko-Boateng O, Aning J. Performance of machine learning algorithms with different K values in K-fold CrossValidation. Int J Inf Technol Comput Sci 2021; 13(6):61-71.

[66]	Zhai X, Yin Y, Pellegrino JW, Haudek KC, Shi L. Applying machine learning in science assessment: a systematic review. Stud Sci Educ 2020; 56(1):111-51.

[67]	Linardatos P, Papastefanopoulos V, Kotsiantis S. Explainable ai: a review of machine learning interpretability methods. Entropy 2020; 23(1):18.

[68]	Antwarg L, Miller RM, Shapira B, Rokach L. Explaining anomalies detected by autoencoders using shapley additive explanations. Expert Syst Appl 2021; 186:115736.

[69]

Lundberg SM, Lee S. A unified approach to interpreting model predictions. In: von Luxburg U, Guyon I, Bengio S, Wallach H, Fergus R, editors. Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS); 2017 Dec 4-9; Long Beach, CA, USA. Red Hook: Curran Associates Inc.; 2017. p. 4768-77.

[70]	Greenwell BM. pdp: an R package for constructing partial dependence plots. R J 2017; 9(1):421.

[71]	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 2021; 34:101822.

[72]	Amlashi AT, Abdollahi SM, Goodarzi S, Ghanizadeh AR. Soft computing based formulations for slump, compressive strength, and elastic modulus of bentonite plastic concrete. J Clean Prod 2019; 230:1197-216.

[73]	Yang LF, Lai BL, Xu R, Hu X, Su H, Cusatis G, et al. Prediction of alkali-silica reaction expansion of concrete using artificial neural networks. Cem Concr Compos 2023; 140:105073.

[74]	Lyngdoh GA, Zaki M, Krishnan N, Das S. Prediction of concrete strengths enabled by missing data imputation and interpretable machine learning. Cem Concr Compos 2022; 128:104414.

[75]	Machello C, Aghabalaei Baghaei K, Bazli M, Hadigheh A, Rajabipour A, Arashpour M, et al. Tree-based machine learning approach to modelling tensile strength retention of fibre reinforced polymer composites exposed to elevated temperatures. Compos, Part B Eng 2024; 270:111132.

[76]	Han TH, Bhat R, Ponduru SA, Sarkar A, Huang J, Sant G, et al. Deep learning to predict the hydration and performance of fly ash-containing cementitious binders. Cem Concr Res 2023; 165:107093.

[77]	Jaf DKI, Abdulrahman PI, Mohammed AS, Kurda R, Qaidi SMA, Asteris PG. Machine learning techniques and multi-scale models to evaluate the impact of silicon dioxide (SiO₂) and calcium oxide (CaO) in fly ash on the compressive strength of green concrete. Constr Build Mater 2023; 400:132604.

[78]

Al Martini S, Sabouni R, Khartabil A, Wakjira TG, Shahria AM. Development and strength prediction of sustainable concrete having binary and ternary cementitious blends and incorporating recycled aggregates from demolished UAE buildings: experimental and machine learning-based studies. Constr Build Mater 2023; 380:131278.

[79]	Khan MI, Abbas YM, Fares G, Alqahtani FK. Strength prediction and optimization for ultrahigh-performance concrete with low-carbon cementitious materials-XG boost model and experimental validation. Constr Build Mater 2023; 387:131606.

[80]	Adel H, Palizban S, SharifiSS, Ilchi Ghazaan M, Habibnejad KA. Predicting mechanical properties of carbon nanotube-reinforced cementitious nanocomposites using interpretable ensemble learning models. Constr Build Mater 2022; 354:129209.

[81]	Almustafa MK, Nehdi ML. Machine learning prediction of structural response of steel fiber-reinforced concrete beams subjected to far-field blast loading. Cem Concr Compos 2022; 126:104378.

[82]	Li H, Lin JJ, Zhao DW, Shi G, Wu H, Wei T, et al. A BFRC compressive strength prediction method via kernel extreme learning machine-genetic algorithm. Constr Build Mater 2022; 344:128076.

[83]	Yang JL, Zeng BW, Ni Z, Fan Y, Hang Z, Wang Y, et al. Comparison of traditional and automated machine learning approaches in predicting the compressive strength of graphene oxide/cement composites. Constr Build Mater 2023; 394:132179.

[84]	Liu KH, Dai ZH, Zhang RB, Zheng J, Zhu J, Yang X. Prediction of the sulfate resistance for recycled aggregate concrete based on ensemble learning algorithms. Constr Build Mater 2022; 317:125917.

[85]	Zhang JR, Niu WJ, Yang YZ, Hou D, Dong B. Machine learning prediction models for compressive strength of calcined sludge-cement composites. Constr Build Mater 2022; 346:128442.

[86]	Aghabalaei Baghaei K, Hadigheh SA. Durability assessment of FRP-to-concrete bonded connections under moisture condition using data-driven machine learning-based approaches. Compos Struct 2021:114576.

[87]	Xue J, Shao JF, Burlion N. Estimation of constituent properties of concrete materials with an artificial neural network based method. Cem Concr Res 2021; 150:106614.

[88]	Charrier M, Ouellet-Plamondon CM. Artificial neural network for the prediction of the fresh properties of cementitious materials. Cem Concr Res 2022; 156:106761.

[89]	Han FL, Lv Y, Liu Y, Zhang X, Yu W, Cheng C, et al. Exploring interpretable ensemble learning to predict mechanical strength and thermal conductivity of aerogel-incorporated concrete. Constr Build Mater 2023; 392:131781.

[90]	Shahmansouri AA, Yazdani M, Hosseini M, Akbarzadeh Bengar H, Farrokh GH. The prediction analysis of compressive strength and electrical resistivity of environmentally friendly concrete incorporating natural zeolite using artificial neural network. Constr Build Mater 2022; 317:125876.

[91]	Cai R, Han TH, Liao WY, Huang J, Li D, Kumar A, et al. Prediction of surface chloride concentration of marine concrete using ensemble machine learning. Cem Concr Res 2020; 136:106164.

[92]	Asteris PG, Skentou AD, Bardhan A, Samui P, Pilakoutas K. Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models. Cem Concr Res 2021; 145:106449.

[93]	Liu KH, Alam MS, Zhu J, Zheng J, Chi L. Prediction of carbonation depth for recycled aggregate concrete using ANN hybridized with swarm intelligence algorithms. Constr Build Mater 2021; 301:124382.

[94]	Zhang TR, Zhang YZ, Wang QH, Aganyira AK, Fang Y. Experimental study and machine learning prediction on compressive strength of spontaneous-combustion coal gangue aggregate concrete. J Build Eng 2023; 71:106518.

[95]	Peng YM, Unluer C. Modeling the mechanical properties of recycled aggregate concrete using hybrid machine learning algorithms. Resour Conserv Recycl 2023; 190:106812.

[96]	Alabdullah AA, Iqbal M, Zahid M, et al. Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis. Constr Build Mater 2022; 345:128296.

[97]	Munir MJ, Kazmi S, Wu YF, Lin X, Ahmad MR. Development of novel design strength model for sustainable concrete columns: a new machine learning-based approach. J Clean Prod 2022; 357:131988.

[98]	Zhang LV, Marani A, Nehdi ML. Chemistry-informed machine learning prediction of compressive strength for alkali-activated materials. Constr Build Mater 2022; 316:126103.

[99]	Zou D, Wu L, Hao Y, Xu L, Chen JJ. Composition-strength relationship study of ultrahigh performance fiber reinforced concrete (UHPFRC) using an interpretable data-driven approach. Constr Build Mater 2023; 392:131973.

[100]

Feng WH, Wang YF, Sun JB, Tang Y, Wu D, Jiang Z, et al. Prediction of thermo-mechanical properties of rubber-modified recycled aggregate concrete. Constr Build Mater 2022; 318:125970.

[101]

Ford E, Maneparambil K, Kumar A, Sant G, Neithalath N. Transfer (machine) learning approaches coupled with target data augmentation to predict the mechanical properties of concrete. Mach Learn Appl 2022; 8:100271.

[102]

Ke XY, Duan Y. A Bayesian machine learning approach for inverse prediction of high-performance concrete ingredients with targeted performance. Constr Build Mater 2021; 270:121424.

[103]

Shen JL, Li Y, Lin H, Li H, Lv J, Feng S, et al. Prediction of compressive strength of alkali-activated construction demolition waste geopolymers using ensemble machine learning. Constr Build Mater 2022; 360:129600.

[104]

Long WJ, Cheng BY, Luo SY, Li L, Mei L. Interpretable auto-tune machine learning prediction of strength and flow properties for self-compacting concrete. Constr Build Mater 2023; 393:132101.

[105]

Güçlüer K, Özbeyaz A, Göymen S, Günaydın O. A comparative investigation using machine learning methods for concrete compressive strength estimation. Mater Today Commun 2021; 27:102278.

[106]

Chen HG, Yang JJ, Chen XH. A convolution-based deep learning approach for estimating compressive strength of fiber reinforced concrete at elevated temperatures. Constr Build Mater 2021; 313:125437.

[107]

Feng JP, Zhang HW, Gao K, Liao Y, Yang J, Wu G. A machine learning and game theory-based approach for predicting creep behavior of recycled aggregate concrete. Case Stud Constr Mater 2022; 17:e01653.

[108]

Taffese WZ, Espinosa-Leal L. A machine learning method for predicting the chloride migration coefficient of concrete. Constr Build Mater 2022; 348:128566.

[109]

Jong SC, Ong D, Oh E. A novel Bayesian inference method for predicting optimum strength gain in sustainable geomaterials for greener construction. Constr Build Mater 2022; 344:128255.

[110]

Zeng ZY, Zhu ZY, Yao W, Wang Z, Wang C, Wei Y, et al. Accurate prediction of concrete compressive strength based on explainable features using deep learning. Constr Build Mater 2022; 329:127082.

[111]

Yao XF, Lyu X, Sun JB, Wang B, Wang Y, Yang M, et al. AI-based performance prediction for 3D-printed concrete considering anisotropy and steam curing condition. Constr Build Mater 2023; 375:130898.

[112]

Ghosh A, Ransinchung G. Application of machine learning algorithm to assess the efficacy of varying industrial wastes and curing methods on strength development of geopolymer concrete. Constr Build Mater 2022; 341:127828.

[113]

Nyakilla EE, Jun G, Kasimu NA, Robert EF, Innocent N, Mohamedy T, et al. Application of machine learning in the prediction of compressive, and shear bond strengths from the experimental data in oil well cement at 80 °C. Ensemble trees boosting approach. Constr Build Mater 2022; 317:125778.

[114]

Young BA, 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 2019; 115:379-88.

[115]

Rodrigues AP, Marathe S, Fernandes R, Shikha A, Shree N. Comparative analysis of machine learning techniques in the prediction of the strength of structural concrete. Mater Today Proc 2023; 88:6-13.

[116]

Çaliskan A, Demirhan S, Tekin R. Comparison of different machine learning methods for estimating compressive strength of mortars. Constr Build Mater 2022; 335:127490.

[117]

Sevim UK, Bilgic HH, Cansiz OF, Ozturk M, Atis CD. Compressive strength prediction models for cementitious composites with fly ash using machine learning techniques. Constr Build Mater 2021; 271:121584.

[118]

Rajender A, Samanta AK. Compressive strength prediction of metakaolin based high-performance concrete with machine learning. Mater Today Proc 2023. In press.

[119]

Shah S, Chen B, Zahid M, Ahmad MR. Compressive strength prediction of one-part alkali activated material enabled by interpretable machine learning. Constr Build Mater 2022; 360:129534.

[120]

Shamim Ansari S, Muhammad Ibrahim S, Danish Hasan S. Conventional and ensemble machine learning models to predict the compressive strength of fly ash based geopolymer concrete. Mater Today Proc. In press.

[121]

Jayasinghe T, Chen BW, Zhang ZR, et al. Data-driven shear strength predictions of recycled aggregate concrete beams with/without shear reinforcement by applying machine learning approaches. Constr Build Mater 2023; 387:131604.

[122]

Lv ZQ, Jiang AN, Liang B. Development of eco-efficiency concrete containing diatomite and iron ore tailings: mechanical properties and strength prediction using deep learning. Constr Build Mater 2022; 327:126930.

[123]

Biswas R, Li EM, Zhang N, Kumar S, Rai B, Zhou J. Development of hybrid models using metaheuristic optimization techniques to predict the carbonation depth of fly ash concrete. Constr Build Mater 2022; 346:128483.

[124]

Mai H, Nguyen MH, Ly HB. Development of machine learning methods to predict the compressive strength of fiber-reinforced self-compacting concrete and sensitivity analysis. Constr Build Mater 2023; 367:130339.

[125]

Gunes B, Karatosun S, Gunes O. Drilling resistance testing combined with SonReb methods for nondestructive estimation of concrete strength. Constr Build Mater 2023; 362:129700.

[126]

Zhang M, Zhang C, Zhang JF, Wang L, Wang F. Effect of composition and curing on alkali activated fly ash-slag binders: machine learning prediction with a random forest-genetic algorithm hybrid model. Constr Build Mater 2023; 366:129940.

[127]

Feng JP, Zhang HW, Gao K, Liao Y, Gao W, Wu G. Efficient creep prediction of recycled aggregate concrete via machine learning algorithms. Constr Build Mater 2022; 360:129497.

[128]

Zhang C, Zhu ZD, Liu F, Yang Y, Wan Y, Huo W, et al. Efficient machine learning method for evaluating compressive strength of cement stabilized soft soil. Constr Build Mater 2023; 392:131887.

[129]

Nguyen H, Vu T, Vo TP, Thai HT. Efficient machine learning models for prediction of concrete strengths. Constr Build Mater 2021; 266:120950.

[130]

Sathiparan N, Jeyananthan P. Predicting compressive strength of cement-stabilized earth blocks using machine learning models incorporating cement content, ultrasonic pulse velocity, and electrical resistivity. Nondestruct Test Eval 2024; 39(5):1045-69.

[131]

Mai H, Trinh SH, Ly HB. Enhancing compressive strength prediction of roller compacted concrete using machine learning techniques. Measurement 2023; 218:113196.

[132]

Salami BA, Iqbal M, Abdulraheem A, Jalal FE, Alimi W, Jamal A, et al. Estimating compressive strength of lightweight foamed concrete using neural, genetic and ensemble machine learning approaches. Cem Concr Compos 2022; 133:104721.

[133]

Gao XF, Yang J, Zhu H, Xu J. Estimation of rubberized concrete frost resistance using machine learning techniques. Constr Build Mater 2023; 371:130778.

[134]

Basaran B, Kalkan I, Bergil E, Erdal E. Estimation of the FRP-concrete bond strength with code formulations and machine learning algorithms. Compos Struct 2021; 268:113972.

[135]

Sami B, Sami B, Kumar P, Ahmed AN, Amieghemen GE, Sherif MM, et al. Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms. Case Stud Constr Mater 2023; 18:e01893.

[136]

Li QF, Song ZM. High-performance concrete strength prediction based on ensemble learning. Constr Build Mater 2022; 324:126694.

[137]

Tanyildizi H, Marani A, Türk K, Nehdi ML. Hybrid deep learning model for concrete incorporating microencapsulated phase change materials. Constr Build Mater 2022; 319:126146.

[138]

Liang MF, Chang Z, Wan Z, Gan Y, Schlangen E, Šavija B. Interpretable ensemble-machine-learning models for predicting creep behavior of concrete. Cem Concr Compos 2022; 125:104295.

[139]

Hilloulin B, Tran V. Interpretable machine learning model for autogenous shrinkage prediction of low-carbon cementitious materials. Constr Build Mater 2023; 396:132343.

[140]

Imran H, Ibrahim M, Al-Shoukry S, Rustam F, Ashraf I. Latest concrete materials dataset and ensemble prediction model for concrete compressive strength containing RCA and GGBFS materials. Constr Build Mater 2022; 325:126525.

[141]

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

[142]

Cosgun C. Machine learning for the prediction of evaluation of existing reinforced concrete structures performance against earthquakes. Structures 2023; 50:1994-2003.

[143]

Dias PP, Jayasinghe LB, Waldmann D. Machine learning in mix design of Miscanthus lightweight concrete. Constr Build Mater 2021; 302:124191.

[144]

Shah MI, Javed MF, Aslam F, Alabduljabbar H. Machine learning modeling integrating experimental analysis for predicting the properties of sugarcane bagasse ash concrete. Constr Build Mater 2022; 314:125634.

[145]

Nunez I, Nehdi ML. Machine learning prediction of carbonation depth in recycled aggregate concrete incorporating SCMs. Constr Build Mater 2021; 287:123027.

[146]

Chi L, Wang M, Liu KH, Lu S, Kan L, Xia X, et al. Machine learning prediction of compressive strength of concrete with resistivity modification. Mater Today Commun 2023; 36:106470.

[147]

El Mir A, El-Zahab S, Sbartaï ZM, Homsi F, Saliba J, El-Hassan H. Machine learning prediction of concrete compressive strength using rebound hammer test. J Build Eng 2023; 64:105538.

[148]

Eyo EU, Abbey SJ. Machine learning regression and classification algorithms utilised for strength prediction of OPC/by-product materials improved soils. Constr Build Mater 2021; 284:122817.

[149]

Gomaa E, Han TH, Elgawady M, Huang J, Kumar A. Machine learning to predict properties of fresh and hardened alkali-activated concrete. Cem Concr Compos 2021; 115:103863.

[150]

Shamsabadi EA, Roshan N, Hadigheh SA, Nehdi ML, Khodabakhshian A, Ghalehnovi M. Machine learning-based compressive strength modelling of concrete incorporating waste marble powder. Constr Build Mater 2022; 324:126592.

[151]

Almasabha G, Al-Shboul KF, Shehadeh A, Alshboul O. Machine learning-based models for predicting the shear strength of synthetic fiber reinforced concrete beams without stirrups. Structures 2023; 52:299-311.

[152]

Moaf FO, Kazemi F, Abdelgader HS, Kurpińska M. Machine learning-based prediction of preplaced aggregate concrete characteristics. Eng Appl Artif Intell 2023; 123:106387.

[153]

Kazemi F, Asgarkhani N, Jankowski R. Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures. Soil Dyn Earthq Eng 2023; 166:107761.

[154]

Kim HK, Lim Y, Tafesse M, Kim GM, Yang B. Micromechanics-integrated machine learning approaches to predict the mechanical behaviors of concrete containing crushed clay brick aggregates. Constr Build Mater 2022; 317:125840.

[155]

Antunes R. Multivariate regression model for peak temperatures in massive elements statistically verified by artificial neural networks. Constr Build Mater 2022; 316:126072.

[156]

Go C, Kwak YJ, Kwag S, Eem S, Lee S, Ju B. On developing accurate prediction models for residual tensile strength of GFRP bars under alkaline-concrete environment using a combined ensemble machine learning methods. Case Stud Constr Mater 2023; 18:e02157.

[157]

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 2022; 352:129019.

[158]

Fan DQ, Yu R, Fu SY, Yue L, Wu C, Shui Z, et al. Precise design and characteristics prediction of Ultra-High Performance Concrete (UHPC) based on artificial intelligence techniques. Cem Concr Compos 2021; 122:104171.

[159]

Jeon D, Jung J, Park J, Min J, Oh JE, Moon J, et al. Predicting airborne chloride deposition in marine bridge structures using an artificial neural network model. Constr Build Mater 2022; 337:127623.

[160]

Bhat R, Han TH, Ponduru SA, Reka A, Huang J, Sant G, et al. Predicting compressive strength of alkali-activated systems based on the network topology and phase assemblages using tree-structure computing algorithms. Constr Build Mater 2022; 336:127557.

[161]

Adesanya E, Aladejare A, Adediran A, Lawal A, Illikainen M. Predicting shrinkage of alkali-activated blast furnace-fly ash mortars using artificial neural network (ANN). Cem Concr Compos 2021; 124:104265.

[162]

Chao ZM, Wang MY, Sun YN, Xu X, Yue W, Yang C, et al. Predicting stress-dependent gas permeability of cement mortar with different relative moisture contents based on hybrid ensemble artificial intelligence algorithms. Constr Build Mater 2022; 348:128660.

[163]

Song HW, Ahmad A, Farooq F, Ostrowski KA, Masłak M, Czarnecki S, et al. Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms. Constr Build Mater 2021; 308:125021.

[164]

Penido RE, de Paixao RCF, Costa LCB, Peixoto RAF, Cury AA, Mendes JC. Predicting the compressive strength of steelmaking slag concrete with machine learning-considerations on developing a mix design tool. Constr Build Mater 2022; 341:127896.

[165]

Wang Z, Liu TX, Long ZL, Wang J, Zhang J. Predicting the drift capacity of precast concrete columns using explainable machine learning approach. Eng Struct 2023; 282:115771.

[166]

Navarrete I, La Fé-Perdomo I, Ramos-Grez JA, et al. Predicting the evolution of static yield stress with time of blended cement paste through a machine learning approach. Constr Build Mater 2023; 371:130632.

[167]

Tanyildizi H. Predicting the geopolymerization process of fly ash-based geopolymer using deep long short-term memory and machine learning. Cem Concr Compos 2021; 123:104177.

[168]

Canbek O, Xu QZ, Mei YJ, Washburn NR, Kurtis KE. Predicting the rheology of limestone calcined clay cements (LC³): linking composition and hydration kinetics to yield stress through Machine Learning. Cem Concr Res 2022; 160:106925.

[169]

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

[170]

Sun YB, Cheng H, Zhang SZ, Mohan MK, Ye G, De Schutter G. Prediction & optimization of alkali-activated concrete based on the random forest machine learning algorithm. Constr Build Mater 2023; 385:131519.

[171]

Fan CC, Zheng YX, Wang SQ, Ma J. Prediction of bond strength of reinforced concrete structures based on feature selection and GWO-SVR model. Constr Build Mater 2023; 400:132602.

[172]

Liu QF, Iqbal MF, Yang J, Lu X, Zhang P, Rauf M. Prediction of chloride diffusivity in concrete using artificial neural network: modelling and performance evaluation. Constr Build Mater 2021; 268:121082.

[173]

Sun Z, Li YL, Li YQ, 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 2023; 36:106565.

[174]

Parhi SK, Patro SK. Prediction of compressive strength of geopolymer concrete using a hybrid ensemble of grey wolf optimized machine learning estimators. J Build Eng 2023; 71:106521.

[175]

Kocak B, Pinarci B, Güvenç U, Kocak Y. Prediction of compressive strengths of pumice-and diatomite-containing cement mortars with artificial intelligence-based applications. Constr Build Mater 2023; 385:131516.

[176]

Cao C. Prediction of concrete porosity using machine learning. Results Eng 2023; 17:100794.

[177]

Dehestani A, Kazemi F, Abdi R, Nitka M. Prediction of fracture toughness in fibre-reinforced concrete, mortar, and rocks using various machine learning techniques. Eng Fract Mech 2022; 276:108914.

[178]

Nagaraju TV, Mantena S, Azab M, Alisha SS, El Hachem C, Adamu M, et al. Prediction of high strength ternary blended concrete containing different silica proportions using machine learning approaches. Results Eng 2023; 17:100973.

[179]

Zhang H, Guo QQ, Xu LY. Prediction of long-term prestress loss for prestressed concrete cylinder structures using machine learning. Eng Struct 2023; 279:115577.

[180]

Amiri M, Hatami F. Prediction of mechanical and durability characteristics of concrete including slag and recycled aggregate concrete with artificial neural networks (ANNs). Constr Build Mater 2022; 325:126839.

[181]

Hosseinzadeh M, Dehestani M, Hosseinzadeh A. Prediction of mechanical properties of recycled aggregate fly ash concrete employing machine learning algorithms. J Build Eng 2023; 76:107006.

[182]

Zhang XQ, Akber MZ, Zheng W. Prediction of seven-day compressive strength of field concrete. Constr Build Mater 2021; 305:124604.

[183]

Chen RY, Song JJ, Xu MB, Wang X, Yin Z, Liu T, et al. Prediction of the corrosion depth of oil well cement corroded by carbon dioxide using GA-BP neural network. Constr Build Mater 2023; 394:132127.

[184]

Congro M, Monteiro V, Brandao A, Santos BF, Roehl D, Silva FA. Prediction of the residual flexural strength of fiber reinforced concrete using artificial neural networks. Constr Build Mater 2021; 303:124502.

[185]

Shanmugasundaram N, Praveenkumar S, Gayathiri K, Divya S. Prediction on compressive strength of engineered cementitious composites using Machine learning approach. Constr Build Mater 2022; 342:127933.

[186]

Mei Y, Sun YH, Li F, Xu X, Zhang A, Shen J. Probabilistic prediction model of steel to concrete bond failure under high temperature by machine learning. Eng Fail Anal 2022; 142:106786.

[187]

Wang NL, Xia ZJ, Amin MN, Ahmad W, Khan K, Althoey F, et al. Sustainable strategy of eggshell waste usage in cementitious composites: an integral testing and computational study for compressive behavior in aggressive environment. Constr Build Mater 2023; 386:131536.

[188]

Mai H, Nguyen MH, Trinh SH, Ly HB. Toward improved prediction of recycled brick aggregate concrete compressive strength by designing ensemble machine learning models. Constr Build Mater 2023; 369:130613.

[189]

El-Sayed AA, Fathy IN, Tayeh BA, Almeshal I. Using artificial neural networks for predicting mechanical and radiation shielding properties of different nano-concretes exposed to elevated temperature. Constr Build Mater 2022; 324:126663.

[190]

Chen N, Zhao SB, Gao ZW, Wang D, Liu P, Oeser M, et al. Virtual mix design: prediction of compressive strength of concrete with industrial wastes using deep data augmentation. Constr Build Mater 2022; 323:126580.

[191]

Chen Z, Lin J, Sagoe-Crentsil K, Duan W. Development of hybrid machine learning-based carbonation models with weighting function. Constr Build Mater 2022; 321:126359.

[192]

Shi Y, Yang Z, Ma S, Kang PL, Shang C, Hu P, et al. Machine learning for chemistry: basics and applications. Engineering 2023; 27:70-83.

[193]

Li Y, Liu YJ, Miao YC, Liu Z, Jiang J, Lin J. Development of heat-resistant tunnel muck-based shotcrete for geothermal environments: dual drive of combining explainable machine learning and microstructure characterization. Constr Build Mater 2025; 473:140994.

[194]

Sobuz MHR, Rahman MM, Aayaz R, Al-Rashed WS, Datta SD, Safayet MA, et al. Combined influence of modified recycled concrete aggregate and metakaolin on high-strength concrete production: experimental assessment and machine learning quantifications with advanced SHAP and PDP analyses. Constr Build Mater 2025; 461:139897.

[195]

Luo X, Li Y, Lin H, Hao M, Wang H. Machine learning-driven modeling and interpretative analysis of drying shrinkage behavior in magnesium silicate hydrate cement. J Build Eng 2025; 107:112721.

[196]

Kondratieva TN, Tyurina VS, Chepurnenko AS. Predicting the risk of early cracking in massive monolithic foundation slabs using artificial intelligence algorithms. Constr Mater Prod 2025; 8(1):6.

[197]

Yao QL, Tu YL, Yang JH. Predicting the compressive strength of solid waste-cement stabilized compacted soil using machine learning model. Mater Today Commun 2025; 44:11882.

[198]

Song C, Xiao R, Zhang C, Zhao X, Sun B. Simulation-free reliability analysis with importance sampling-based adaptive training physics-informed neural networks: method and application to chloride penetration. Reliab Eng Syst Saf 2024; 246:110083.

[199]

Zhang TJ, Wang DL, Lu Y. RheologyNet: a physics-informed neural network solution to evaluate the thixotropic properties of cementitious materials. Cem Concr Res 2023; 168:107157.

[200]

Wan Y, Zheng W, Wang Y. Identification of chloride diffusion coefficient in concrete using physics-informed neural networks. Constr Build Mater 2023; 393:132049.

[201]

Rahman MA, Lu Y. EcoBlendNet: a physics-informed neural network for optimizing supplementary material replacement to reduce the carbon footprint during cement hydration. J Clean Prod 2024; 464:142777.

[202]

Liu Q, Andersen LV, Wu M. Prediction of concrete abrasion depth and computational design optimization of concrete mixtures. Cem Concr Compos 2024; 148:105431.

[203]

Ali Hadi K, Ridha AS. Deep learning techniques in concrete powder mix designing. Open Eng 2024; 14(1):20220588.

[204]

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

[205]

Li Z, Gao Y, Zhu Z, Tian W. Data-guided for discovering high-strength, cost-effective, and low-carbon rice husk ash concrete. J CO₂ Util 2024; 83:102786.

[206]

Cheng B, Mei L, Long W, Luo Q, Zhang J, Xiong C, et al. Data driven multi-objective design for low-carbon self-compacting concrete considering durability. J Clean Prod 2024; 450:141947.

[207]

Wang W, Chen SJ, Duan WH, Sagoe-Crentsil K, Pathirage CSN, Li L, et al. Revealing microstructural modifications of graphene oxide-modified cement via deep learning and nanoporosity mapping: implications for structural materials’ performance. ACS Appl Nano Mater 2022; 5(5):7092-102.

[208]

Szafraniec M, Omiotek Z, Barnat-Hunek D. Water absorption prediction of nanopolymer hydrophobized concrete surface using texture analysis and machine learning algorithms. Constr Build Mater 2023; 375:130969.

[209]

Lin JL, Liu YM, Sui H, Sagoe-Crentsil K, Duan W. Microstructure of graphene oxide-silica-reinforced OPC composites: image-based characterization and nano-identification through deep learning. Cem Concr Res 2022; 154:106737.

[210]

Zhang R, Yan XF, Guo L. Deep learning-based classification of damage-induced acoustic emission signals in UHPC. Constr Build Mater 2022; 356:129885.

[211]

Sui H, Wang W, Lin JL, Tang ZQ, Yang DS, Duan W. Spatial correlation and pore morphology analysis of limestone calcined clay cement (LC³) via machine learning and image-based characterisation. Constr Build Mater 2023; 401:132721.

[212]

Nguyen HD, Wang W, Yao XP, Sagoe-Crentsil K, Duan W. Porosity-modulus mapping enhanced nanomechanical analysis of heterogeneous materials. J Mater Sci 2023; 58(24):10058-72.

[213]

Liang MF, Gan YD, Chang Z, Zhi W, Erik S, Branko Š. Microstructure-informed deep convolutional neural network for predicting short-term creep modulus of cement paste. Cem Concr Res 2022; 152:106681.

[214]

Guo JF, Li MH, Wang L, Yang B, Zhang L, Chen Z, et al. Estimating cement compressive strength using three-dimensional microstructure images and deep belief network. Eng Appl Artif Intell 2020; 88:103378.

[215]

Komninos P, Verraest A, Eleftheroglou N, Zarouchas D. Intelligent fatigue damage tracking and prognostics of composite structures utilizing raw images via interpretable deep learning. Compos B Eng 2024; 287:111863.

[216]

Hou Y, Li Q, Zhang C, Lu G, Ye Z, Chen Y, et al. The state-of-the-art review on applications of intrusive sensing, image processing techniques, and machine learning methods in pavement monitoring and analysis. Engineering 2021; 7(6):845-56.

[217]

Zhang YH, Zheng JQ, Sun W, Shan L. Image recognition method of building wall cracks based on feature distribution. Soft Comput 2020; 24(11):8285-94.

[218]

Zhang Q, Barri K, Babanajad SK, Alavi AH. Real-time detection of cracks on concrete bridge decks using deep learning in the frequency domain. Engineering 2021; 7(12):1786-96.

[219]

Bao Y, Chen Z, Wei S, Xu Y, Tang Z, Li H. The state of the art of data science and engineering in structural health monitoring. Engineering 2019; 5(2):234-42.

[220]

Du B, Ye J, Zhu H, Sun L, Du Y. Intelligent monitoring system based on spatio-temporal data for underground space infrastructure. Engineering 2023; 25:194-203.

[221]

Shi ZQ, Jin N, Chen DB, Ai D. A comparison study of semantic segmentation networks for crack detection in construction materials. Constr Build Mater 2024; 414:134850.

[222]

Shi MN, Hua TB, Yang ZH, Tan C, Wen Y. Weakly supervised deep learning-based concrete aggregates automatic segmentation for assessing separation degree. J Build Eng 2024; 82:108342.

[223]

Sheiati S, Nguyen H, Kinnunen P, Ranjbar N. Cementitious phase quantification using deep learning. Cem Concr Res 2023; 172:107231.

[224]

Yu F, Cai H, Zhang H, Hu M, Zhang R, Gao Z. Automatic identification method for three-phase structure of pervious concrete based on deep learning network of Mask R-CNN. Constr Build Mater 2024; 420:135534.

[225]

Hao ZX, Lu C, Li ZH. Highly accurate and automatic semantic segmentation of multiple cracks in engineered cementitious composites (ECC) under dual pre-modification deep-learning strategy. Cem Concr Res 2023; 165:107066.

[226]

Han YM, Wang L, Wang YQ, Geng Z. Intelligent small sample defect detection of concrete surface using novel deep learning integrating improved YOLOv5. IEEE-CAA J Automatica Sin 2024; 11(2):545-7.

[227]

Liang J, Gu XY, Jiang D, Zhang Q. CNN-based network with multi-scale context feature and attention mechanism for automatic pavement crack segmentation. Autom Constr 2024; 164:105482.

[228]

Jiang YQ, Pang DD, Li CD, Wang J. A method of concrete damage detection and localization based on weakly supervised learning. Comput Aided Civ Infrastruct Eng 2024; 39(7):1042-60.

[229]

Song Y, Huang ZL, Shen CY, Shi H, Lange DA. Deep learning-based automated image segmentation for concrete petrographic analysis. Cem Concr Res 2020; 135:106118.

[230]

Saha SK, Pradhan S, Barai SV. Use of machine learning based technique to X-ray microtomographic images of concrete for phase segmentation at meso-scale. Constr Build Mater 2020; 249:118874.

[231]

Chen W, Iyer A, Bostanabad R. Data centric design: a new approach to design of microstructural material systems. Engineering 2022; 10:89-98.

[232]

Hong SW, Kim SY, Park K, Terada K, Lee H, Han TS. Mechanical property evaluation of 3D multi-phase cement paste microstructures reconstructed using generative adversarial networks. Cem Concr Compos 2024; 152:105646.

[233]

Zhao X, Wang L, Li QF, Chen H, Liu S, Hou P, et al. 3D microstructural generation from 2D images of cement paste using generative adversarial networks. Cem Concr Res 2025; 187:107726.

[234]

Lin JL, Chen SJ, Wang W, Pathirage CSN, Li L, Sagoe-Crentsil K, et al. Transregional spatial correlation revealed by deep learning and implications for material characterisation and reconstruction. Mater Charact 2021; 178:111268.

[235]

Yao XP, Fang HY, Du MR, Feng H, Zhai K, Lin J, et al. Evolution of the microporous structure in cement hydration: a deep learning-based image translation method. J Build Eng 2024; 94:110065.

[236]

Huang B, Kang F, Li XY, Zhu S. Underwater dam crack image generation based on unsupervised image-to-image translation. Autom Constr 2024; 163:105430.

[237]

Li SY, Zhao XF. High-resolution concrete damage image synthesis using conditional generative adversarial network. Autom Constr 2023; 147:104739.

[238]

Qoku E, Xu K, Li J, Monteiro PJM, Kurtis KE. Advances in imaging, scattering, spectroscopy, and machine learning-aided approaches for multiscale characterization of cementitious systems. Cem Concr Res 2023; 174:107335.

[239]

Tong Z, Wang Z, Wang X, Ma Y, Guo H, Liu C. Characterization of hydration and dry shrinkage behavior of cement emulsified asphalt composites using deep learning. Constr Build Mater 2021; 274:121898.

[240]

Zheng XY, Watanabe I, Paik J, Li J, Guo X, Naito M. Text-to-microstructure generation using generative deep learning. Small 2024; 20(37):2402685.

[241]

Zhang H, Xu T, Li H, Zhang S, Wang X, Huang X, et al. StackGAN++: realistic image synthesis with stacked generative adversarial networks. IEEE Trans Pattern Anal Mach Intell 2019; 41(8):1947-62.

[242]

Dong P, Wu L, Li R, Meng X, Meng L. Text to image synthesis with multi-granularity feature aware enhancement generative adversarial networks. Comput Vis Image Underst 2024; 245:104042.

[243]

Tan YX, Lee CP, Neo M, Lim KM, Lim JY. Text-to-image synthesis with self-supervised bi-stage generative adversarial network. Pattern Recognit Lett 2023; 169:43-9.

[244]

Ge X, Goodwin RT, Yu H, Romero P, Abdelrahman O, Sudhalkar A, et al. Accelerated design and deployment of low-carbon concrete for data centers. In: Bainomugisha E, Brunette W, Dell N, Sharma A, editors. Proceedings of the ACM International Conference Proceeding Series: 2022 Jun 29-Jul 1; Seattle, WA, USA. New York City: Association for Computing Machinery (ACM); 2022. p. 340-52.

[245]

Converge launches AI machine learning program for concrete strength prediction [Internet]. Greater Manchester: Highways Today; 2019 Nov 27 [2025 May 21]. Available from: https://highways.today/2019/11/27/converge-ai-concrete-strength/.

[246]

Mirzazade A, Popescu C, Blanksvärd T, Täljsten B. Workflow for off-site bridge inspection using automatic damage detection-case study of the Pahtajokk Bridge. Remote Sens 2021; 13(14):2665.

[247]

Nguyen CL, Nguyen A, Brown J, Byrne T, Ngo BT, Luong CX. Optimising concrete crack detection: a study of transfer learning with application on NVIDIA Jetson Nano. Sensors 2024; 24(23):7818.

[248]

Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Biol 2022; 23(1):40-55.

[249]

Qi XB, Chen GF, Li Y, Cheng X, Li C. Applying neural-network-based machine learning to additive manufacturing: current applications, challenges, and future perspectives. Engineering 2019; 5(4):721-9.

[250]

Fiona Zhao Y, Xie J, Sun L. On the data quality and imbalance in machine learning-based design and manufacturing—a systematic review. Engineering 2025; 45:105-31.

[251]

Tezsezen E, Yigci D, Ahmadpour A, Tasoglu S. AI-based metamaterial design. ACS Appl Mater Interfaces 2024; 16(23):29547-69.

[252]

Zhang Z, Li S, Feng H, Zhou X, Xu N, Li H, et al. Machine learning for bridge wind engineering. Adv Wind Eng 2024; 1(1):100002.

[253]

Dobbelaere MR, Plehiers PP, Van de Vijver R, Stevens CV, Van Geem KM. Machine learning in chemical engineering: strengths, weaknesses, opportunities, and threats. Engineering 2021; 7(9):1201-11.

[254]

Gao Y, Wang JY, Xu XX. Machine learning in construction and demolition waste management: progress, challenges, and future directions. Autom Constr 2024; 162:105380.

[255]

Dev B, Rahman MA, Islam MJ, Rahman MZ, Zhu D. Properties prediction of composites based on machine learning models: a focus on statistical index approaches. Mater Today Commun 2024; 38:107659.