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人工智能(AI)技术为可持续混凝土研发开辟了新途径，能够在保障材料性能的同时，有效平衡成本、碳足迹和能耗等可持续性指标。本文系统回顾了面向可持续混凝土的AI技术，重点讨论了其可解释性和可信赖性提升方法。首先，总结了传统AI与先进AI方法在提升混凝土可持续性方面的应用现状；随后，分析了数据“质”与“量”、预测可靠性、多目标优化设计以及更广泛的社会性考量等当前面临的关键挑战；同时探讨了将AI与机理模型、全生命周期评估模型及信息物理系统相结合，以实现自动化与自适应混凝土生产的潜在路径；最后，提出了未来的研究方向，并规划了基于AI的可持续混凝土基础设施发展路径。旨在启发研究人员与工程师利用AI技术，共同推动绿色建筑环境的构建。
Abstract：Concrete is the most widely used construction material with an annual consumption of about 30 billion tons. However, the production of concrete creates significant environmental problems. Cement is a key binder in concrete, and its production requires burning limestone in high-temperature kilns, which causes approximately 8% of global carbon dioxide emissions. As urbanization and infrastructure construction continue to grow rapidly, the demand for cement and concrete is expected to be increased by 12%–23% by 2050. If we do not develop sustainable alternatives, this will affect a climate change. To address this issue, the industry is increasingly using supplementary cementitious materials, such as fly ash, slag, and calcined clay. In addition, using solid waste like mine tailings and demolition waste also becomes an important strategy. These methods can reduce the use of cement clinkers and keep waste out of landfills, providing both environmental and economic benefits. However, incorporating these diverse materials makes the design process much harder. Conventional design methods rely on experience and trial-and-error experiments. This process is time-consuming, consumes many resources, and cannot fully reflect the complex interactions between different materials. For instance, mixing multiple types of waste materials creates a high-dimensional design space that is hard to manage with simple, linear experiments. Artificial Intelligence(AI) technology offers an effective way to solve these challenges. Machine learning models can accurately predict key properties of concrete, such as fresh properties, mechanical strength, and durability, based on the historical experimental data. This review systematically summarizes AI techniques for sustainable concrete. The research moves from simple data mapping to more intelligent systems. Initially, some classic models like artificial neural networks and support vector machines are used. These models act as a map between the ingredients(inputs) and the performance(outputs). They can be used for capturing non-linear relationships that conventional equations cannot obtain. For instance, models like XGBoost and Random Forest can predict compressive strength and chloride resistance with a high accuracy, even when using relatively small datasets. These models are often used in a design loop, i.e., predicting performance and using optimization algorithms to adjust the mixture, balancing high strength with lower cost and reduced carbon emissions. However, conventional AI models have a major problem, i.e., they are often "black boxes". This means engineers cannot see how the model makes decisions, leading to a lack of trust in safety-critical projects. Also, these models depend heavily on data quality, and the existing datasets in concrete science are often small, inconsistent, or lack standard formatting. To fix this issue, the focus is shifting to advanced AI models. This review highlights three key improvements. First, data augmentation techniques, such as generative adversarial networks, are used to create synthetic data. This can solve the problem of small datasets via learning the patterns of real data and generating new, realistic samples. Second, explainable AI is used to open the "black box". Methods like SHAP analysis can show exactly how much each ingredient contributes to the final strength, making the results easier to understand and verify. Third, knowledge-guided learning combines data with physical laws. AI models can follow scientific principles via adding physical constraints(like diffusion laws) or using Knowledge Graphs, making them more reliable and scientifically correct compared to pure data-driven models. Summary and Prospects This review concludes that AI makes a great progress in predicting performance and optimizing mixtures, while there are still some challenges before it is widely used in real engineering. The main challenges include the lack of high-quality open datasets, the poor ability of models to work on new materials(generalization), and the need for better trust and interpretability. Future research needs to move from static predictions to autonomous systems. In the future, an AI-driven autonomous system can be used for sustainable concrete. This involves building a cyber-physical system. In this system, sensors collect real-time data during concrete production. AI models can analyze the data and automatically adjust the mixture proportions to ensure consistent quality. This allows for adaptive manufacturing, reducing waste and ensuring that the concrete meets the design requirements. Furthermore, Digital Twin technology can extend this to the entire life of the structure. A digital twin is a virtual copy of the physical infrastructure. It can update itself using sensor data to monitor the health of the structure and calculate its carbon footprint in real time. This can favor to make better decisions about maintenance and repair, extending the service life of the infrastructure. Finally, we need to focus on five priorities, i.e., creating open benchmark datasets for fair comparison; developing physics-informed AI that combines data with expert knowledge; advancing multi-objective optimization to balance performance, cost, and carbon; building cyber-physical systems and digital twins; and establishing standards and training programs. AI should not replace human experts. Instead, it should act as an intelligent assistant, helping engineers to design greener and more durable concrete. This human-AI collaboration is a key to achieving a carbon-neutral built environment in the future.
[1]YORK I N, Europe I. Concrete needs to lose its colossal carbon footprint[J]. Nature, 2021, 597(7878):593–594.
[2]QIAN X, ZHOU X Y, HU C L, et al. Role of partial limestone calcination in carbonated lime-based binders[J]. Cem Concr Res, 2024,183:107572.
[3]MA X S, HU H B, LUO Y, et al. A carbon footprint assessment for usage of recycled aggregate and supplementary cementitious materials for sustainable concrete:A life-cycle perspective in China[J]. J Clean Prod, 2025, 490:144772.
[4]RIHNER M C S, Hafez H, Walkley B, et al. Thousand cuts:a realistic route to decarbonise the UK cement and concrete sector by 2050[J].Sustainable Prod Consumption, 2025, 58:319–333.
[5]JIN S S, ZHANG Y, YAN Y L, et al. Influence of aggregate characteristics on the plant growing environment of the planting concrete with SAC[J]. J Clean Prod, 2024, 445:141179.
[6]RATHNAYAKA M, KARUNASINGHE D, GUNASEKARA C, et al.Machine learning approaches to predict compressive strength of fly ash-based geopolymer concrete:A comprehensive review[J]. Constr Build Mater, 2024, 419:135519.
[7]BHAGATH SINGH G V P, DURGA PRASAD V. Environmental impact of concrete containing high volume fly ash and ground granulated blast furnace slag[J]. J Clean Prod, 2024, 448:141729.
[8]ABBAS S N, QURESHI M I, ALKHARISI M K, et al. Combined effect of silica fume and various fibers on fresh and hardened properties of concrete incorporating HDPE aggregates[J]. Constr Build Mater,2024, 445:137940.
[9]AL KINDI H, ABDEL-GAWWAD H A, MEDDAH M S, et al. An overview of the critical influential parameters on the performance of limestone calcined clay cement paste, mortar, and concrete[J]. Constr Build Mater, 2024, 444:137615.
[10]ABBADI A, MUCSI G. A review on complex utilization of mine tailings:Recovery of rare earth elements and residue valorization[J]. J Environ Chem Eng, 2024, 12(3):113118.
[11]LIU Y L, LIN A, THOMPSON J, et al. Per-and polyfluoroalkyl substances(PFAS)in construction and demolition debris(CDD):Discerning sources and fate during waste management[J]. J Hazard Mater, 2024, 472:134567.
[12]LEE M Y, WANG W L, DU Y, et al. Applications of UV/H2O2,UV/persulfate, and UV/persulfate/Cu2+for the elimination of reverse osmosis concentrate generated from municipal wastewater reclamation treatment plant:Toxicity, transformation products, and disinfection byproducts[J]. Sci Total Environ, 2021, 762:144161.
[13]MOHTASHAM MOEIN M, SARADAR A, RAHMATI K, et al.Predictive models for concrete properties using machine learning and deep learning approaches:A review[J]. J Build Eng, 2023, 63:105444.
[14]LI Z Z, YOON J, ZHANG R, et al. Machine learning in concrete science:Applications, challenges, and best practices[J]. NPJ Comput Mater, 2022, 8:127.
[15]SONG H W, AHMAD A, FAROOQ F, et al. Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms[J]. Constr Build Mater, 2021, 308:125021.
[16]ASTM International Committee C09 on Concrete and Concrete Aggregates. Standard test method for compressive strength of cylindrical concrete specimens[M]. West Conshohocken:ASTM International, 2014.
[17]VAN QUAN TRAN, DANG V Q, HO L S. Evaluating compressive strength of concrete made with recycled concrete aggregates using machine learning approach[J]. Constr Build Mater, 2022, 323:126578.
[18]ASTERIS P G, SKENTOU A D, BARDHAN A, et al. Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models[J]. Cem Concr Res, 2021, 145:106449.
[19]ZIYAD SAMI B H, ZIYAD SAMI B F, KUMAR P, et al. Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms[J]. Case Stud Constr Mater, 2023,18:e01893.
[20]GOMAA E, HAN T H, ELGAWADY M, et al. Machine learning to predict properties of fresh and hardened alkali-activated concrete[J].Cem Concr Compos, 2021, 115:103863.
[21]PARICHATPRECHA R, Nimityongskul P. Analysis of durability of high performance concrete using artificial neural networks[J]. Constr Build Mater, 2009, 23(2):910–917.
[22]DEROUSSEAU M A, Kasprzyk J R, Srubar III W V. Multi-objective optimization methods for designing low-carbon concrete mixtures[J].Frontiers in Materials, 2021, 8:680895.
[23]BUFFENBARGER J K, Casilio J M, AzariJafari H, et al. Role of mixture overdesign in the sustainability of concrete:current state and future perspective[J]. ACI Mater J, 2023, 120(1):89–100.
[24]TAFFESE W Z, Hilloulin B, Zaccardi Y V, et al. Machine learning in concrete durability:challenges and pathways identified by RILEM TC315-DCS towards enhanced predictive models[J]. Mater Struct, 2025,58:145.
[25]DWIVEDI R, DAVE D, NAIK H, et al. Explainable AI(XAI):Core ideas, techniques, and solutions[J]. ACM Comput Surv, 2023, 55(9):1–33.
[26]KARNIADAKIS G E, KEVREKIDIS I G, LU L, et al.Physics-informed machine learning[J]. Nat Rev Phys, 2021, 3:422–440.
[27]GUO P W, MENG W N, BAO Y. Knowledge graph-guided data-driven design of ultra-high-performance concrete(UHPC)with interpretability and physicochemical reaction discovery capability[J]. Constr Build Mater, 2024, 430:136502.
[28]GUO P W, MENG W N, BAO Y. Knowledge-guided data-driven design of ultra-high-performance geopolymer(UHPG)[J]. Cem Concr Compos,2024, 153:105723.
[29]BEN CHAABENE W, FLAH M, NEHDI M L. Machine learning prediction of mechanical properties of concrete:Critical review[J].Constr Build Mater, 2020, 260:119889.
[30]ZHENG W, SHUI Z H, XU Z Z, et al. Multi-objective optimization of concrete mix design based on machine learning[J]. J Build Eng, 2023,76:107396.
[31]MANSOUR E, DHASMANA H, MOUSA M R, et al. Machine learning-based technology for asphalt concrete pavement performance decision-making in hot and humid climates[J]. Constr Build Mater,2024, 442:137625.
[32]NGUYEN H, VU T, VO T P, et al. Efficient machine learning models for prediction of concrete strengths[J]. Constr Build Mater, 2021, 266:120950.
[33]NASERI H, JAHANBAKHSH H, HOSSEINI P, et al. Designing sustainable concrete mixture by developing a new machine learning technique[J]. J Clean Prod, 2020, 258:120578.
[34]ZHANG J F, HUANG Y M, WANG Y H, et al. Multi-objective optimization of concrete mixture proportions using machine learning and metaheuristic algorithms[J]. Constr Build Mater, 2020, 253:119208.
[35]WANG S Q, XIA P, CHEN K Y, et al. Prediction and optimization model of sustainable concrete properties using machine learning, deep learning and swarm intelligence:A review[J]. J Build Eng, 2023, 80:108065.
[36]GUO P W, MAHJOUBI S, LIU K J, et al. Self-updatable AI-assisted design of low-carbon cost-effective ultra-high-performance concrete(UHPC)[J]. Case Stud Constr Mater, 2023, 19:e02625.
[37]TAM V W Y, BUTERA A, LE K N, et al. A prediction model for compressive strength of CO2 concrete using regression analysis and artificial neural networks[J]. Constr Build Mater, 2022, 324:126689.
[38]RAHMAN S K, AL-AMERI R. Structural assessment of Basalt FRP reinforced self-compacting geopolymer concrete using artificial neural network(ANN)modelling[J]. Constr Build Mater, 2023, 397:132464.
[39]BISWAS R, KUMAR M, SINGH R K, et al. A novel integrated approach of RUNge Kutta optimizer and ANN for estimating compressive strength of self-compacting concrete[J]. Case Stud Constr Mater, 2023, 18:e02163.
[40]ZHENG Z S, TIAN C, WEI X S, et al. Numerical investigation and ANN-based prediction on compressive strength and size effect using the concrete mesoscale concretization model[J]. Case Stud Constr Mater, 2022, 16:e01056.
[41]GOLAFSHANI E M, BEHNOOD A. Predicting the mechanical properties of sustainable concrete containing waste foundry sand using multi-objective ANN approach[J]. Constr Build Mater, 2021, 291:123314.
[42]LIU Y, CAO Y, WANG L, et al. Prediction of the durability of high-performance concrete using an integrated RF-LSSVM model[J].Constr Build Mater, 2022, 356:129232.
[43]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]. J Build Eng, 2022, 60:105146.
[44]SUN Z, LI Y L, YANG Y X, et al. Splitting tensile strength of basalt fiber reinforced coral aggregate concrete:Optimized XGBoost models and experimental validation[J]. Constr Build Mater, 2024, 416:135133.
[45]AL-TAAI S R, AZIZE N M, THOENY Z A, et al. XGBoost prediction model optimized with Bayesian for the compressive strength of eco-friendly concrete containing ground granulated blast furnace slag and recycled coarse aggregate[J]. Appl Sci, 2023, 13(15):8889.
[46]AKBER M Z, ALI ANWAR G, CHAN W K, et al. TPE-xgboost for explainable predictions of concrete compressive strength considering compositions, and mechanical and microstructure properties of testing samples[J]. Constr Build Mater, 2024, 457:139398.
[47]PASWAN R K, GOGINENI A, SHARMA S, et al. Predicting split tensile strength in Portland and geopolymer concretes using machine learning algorithms:A comparative study[J]. J Build Pathol Rehabil,2024, 9(2):129.
[48]EL MAHDI SAFHI A, DABIRI H, SOLIMAN A, et al. Prediction of self-consolidating concrete properties using XGBoost machine learning algorithm:Part 1–Workability[J]. Constr Build Mater, 2023, 408:133560.
[49]EL MAHDI SAFHI A, DABIRI H, SOLIMAN A, et al. Prediction of self-consolidating concrete properties using XGBoost machine learning algorithm:Rheological properties[J]. Powder Technol, 2024, 438:119623.
[50]JIANG C M, CHEN G, LI S X, et al. Machine learning-aided prediction and genetic algorithm optimization of sulfate resistance in Portland cement-based materials[J]. Constr Build Mater, 2025, 494:143334.
[51]ABDULALIM ALABDULLAH A, 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[J]. Constr Build Mater, 2022, 345:128296.
[52]CELIK G, OZDEMIR M. Determination of concrete compressive strength from surface images with the integration of CNN and SVR methods[J]. Measurement, 2024, 238:115331.
[53]HILLOULIN B, UMUNNAKWE R. Machine learning-aided prediction of shrinkage in modern concrete:Focus on mix proportions and SCMs[J]. J Build Eng, 2024, 98:111410.
[54]BOURAS Y, LI L. Prediction of high-temperature creep in concrete using supervised machine learning algorithms[J]. Constr Build Mater,2023, 400:132828.
[55]SAFIEH H, HAWILEH R A, ASSAD M, et al. Using multiple machine learning models to predict the strength of UHPC mixes with various FA percentages[J]. Infrastructures, 2024, 9(6):92.
[56]YOUNG B A, HALL A, PILON L, et al. Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?:New insights from statistical analysis and machine learning methods[J].Cem Concr Res, 2019, 115:379–388.
[57]KHAN M I, ABBAS Y M, FARES G, et al. Strength prediction and optimization for ultrahigh-performance concrete with low-carbon cementitious materials–XG boost model and experimental validation[J].Constr Build Mater, 2023, 387:131606.
[58]LI Q F, SONG Z M. High-performance concrete strength prediction based on ensemble learning[J]. Constr Build Mater, 2022, 324:126694.
[59]JIA J F, CHEN X Z, BAI Y L, et al. An interpretable ensemble learning method to predict the compressive strength of concrete[J]. Structures,2022, 46:201–213.
[60]WANG X L, CHEN A R, LIU Y Q. Explainable ensemble learning model for predicting steel section-concrete bond strength[J]. Constr Build Mater, 2022, 356:129239.
[61]TAFFESE W Z, ZHU Y P, CHEN G D. Ensemble-learning model based ultimate moment prediction of reinforced concrete members strengthened by UHPC[J]. Eng Struct, 2024, 305:117705.
[62]CHOU J S, PHAM A D. Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength[J]. Constr Build Mater, 2013, 49:554–563.
[63]ERDAL H I. Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction[J]. Eng Appl Artif Intell, 2013, 26(7):1689–1697.
[64]YU B, CHENG H, YU Z C, et al. Physics-supervised ensemble learning model for predicting failure modes of reinforced concrete columns[J].Eng Struct, 2023, 292:116560.
[65]LIANG D, XUE F. Integrating automated machine learning and interpretability analysis in architecture, engineering and construction industry:A case of identifying failure modes of reinforced concrete shear walls[J]. Comput Ind, 2023, 147:103883.
[66]CHEN H Y, CAO Y, LIU Y, et al. Enhancing the durability of concrete in severely cold regions:Mix proportion optimization based on machine learning[J]. Constr Build Mater, 2023, 371:130644.
[67]TOPÇUİB, SARıDEMIR M. Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic[J]. Comput Mater Sci, 2008, 41(3):305–311.
[68]KARTHIKA S, DURGADEVI M. Generative adversarial network(GAN):A general review on different variants of GAN and applications[C]//2021 6th International Conference on Communication and Electronics Systems(ICCES). Coimbatre, India. IEEE, 2021:1–8.
[69]MAHJOUBI S, MENG W N, BAO Y. Auto-tune learning framework for prediction of flowability, mechanical properties, and porosity of ultra-high-performance concrete(UHPC)[J]. Appl Soft Comput, 2022,115:108182.
[70]EKANAYAKE I U, MEDDAGE D P P, RATHNAYAKE U. A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations(SHAP)[J]. Case Stud Constr Mater, 2022, 16:e01059.
[71]WU R J, XIA Y, XIA J. Long-term prediction of surface chloride content of marine concrete structures using inverse physics-informed neural networks[J]. Eng Struct, 2025, 329:119752.
[72]GUO P W, MENG W N, BAO Y. Intelligent characterization of complex cracks in strain-hardening cementitious composites based on generative computer vision[J]. Constr Build Mater, 2024, 411:134812.
[73]DUAN S D, TAN X, GUO P W, et al. The transformative roles of generative artificial intelligence in vision techniques for structural health monitoring:A state-of-the-art review[J]. Adv Eng Inform, 2025,68:103719.
[74]GUO P W, MENG X J, MENG W N, et al. Automatic assessment of concrete cracks in low-light, overexposed, and blurred images restored using a generative AI approach[J]. Autom Constr, 2024, 168:105787.
[75]KARIM R, ISLAM M H, DATTA S D, et al. Synergistic effects of supplementary cementitious materials and compressive strength prediction of concrete using machine learning algorithms with SHAP and PDP analyses[J]. Case Stud Constr Mater, 2024, 20:e02828.
[76]MISHRA S, Sturm B L, Dixon S. Local interpretable model-agnostic explanations for music content analysis[C]//18th International Society for Music Information Retrieval Conference, Suzhou, China, 2017:537–543.
[77]ALAWI AL-NAGHI A A, AAMIR K, AMIN M N, et al. Predicting strength in polypropylene fiber reinforced rubberized concrete using symbolic regression AI techniques[J]. Case Stud Constr Mater, 2025, 23:e05024.
[78]TU L, ZHAO H, TAN C J, et al. Tensile behavior of reinforced UHPC:Effects of autogenous shrinkage and model of tensile capacity via deep learning-based symbolic regression[J]. Cem Concr Compos, 2025, 160:106019.
[79]GOLAFSHANI E M, ASHOUR A. Prediction of self-compacting concrete elastic modulus using two symbolic regression techniques[J].Autom Constr, 2016, 64:7–19.
[80]MEGAHED K. Symbolic regression for strength prediction of eccentrically loaded concrete-filled steel tubular columns[J]. Sci Rep,2025, 15:3085.
[81]ONYELOWE K C, KAMCHOOM V, EBID A M, et al. Optimizing the utilization of Metakaolin in pre-cured geopolymer concrete using ensemble and symbolic regressions[J]. Sci Rep, 2025, 15(1):6858.
[82]LE B A, NGUYEN H Q, TRAN B V, et al. Symbolic regression model for predicting compression strength of steel fiber-reinforced concrete[J].Transp Res Procedia, 2025, 85:94–101.
[83]YU X R. Developing an artificial neural network model to predict the durability of the RC beam by machine learning approaches[J]. Case Stud Constr Mater, 2022, 17:e01382.
[84]SHABAN W M, ELBAZ K, ZHOU A N, et al. Physics-informed deep neural network for modeling the chloride diffusion in concrete[J]. Eng Appl Artif Intell, 2023, 125:106691.
[85]DAS P, KASHEM A. Hybrid machine learning approach to prediction of the compressive and flexural strengths of UHPC and parametric analysis with shapley additive explanations[J]. Case Stud Constr Mater,2024, 20:e02723.
[86]GUZMÁN-TORRES J A, DOMÍNGUEZ-MOTA F J, TINOCOGUERRERO G, et al. Extreme fine-tuning and explainable AI model for non-destructive prediction of concrete compressive strength, the case of ConcreteXAI dataset[J]. Adv Eng Softw, 2024, 192:103630.
[87]AL-BASHITI M K, NASER M Z. Verifying domain knowledge and theories on Fire-induced spalling of concrete through eXplainable artificial intelligence[J]. Constr Build Mater, 2022, 348:128648.
[88]JIN X X, YANG X C, JIANG Y X, et al. An AI-based partial explainable prediction of rubber concrete strength on mobile devices[J].Constr Build Mater, 2024, 427:136234.
[89]ONYELOWE K C, KONTONI D N, EBID A M, et al. Multi-objective optimization of sustainable concrete containing fly ash based on environmental and mechanical considerations[J]. Buildings, 2022,12(7):948.
[90]GAN V J L, CHENG J C P, LO I M C. Integrating life cycle assessment and multi-objective optimization for economical and environmentally sustainable supply of aggregate[J]. J Clean Prod, 2016, 113:76–85.
[91]TANHADOUST A, ALI EMADI S A, NASROLLAHPOUR S, et al.Optimal design of sustainable recycled rubber-filled concrete using life cycle assessment and multi-objective optimization[J]. Constr Build Mater, 2023, 402:132878.
[92]GOLEWSKI G L. Assessing of water absorption on concrete composites containing fly ash up to 30%in regards to structures completely immersed in water[J]. Case Stud Constr Mater, 2023, 19:e02337.
[93]WANG Y H, XIAO R, LU H, et al. Effect of curing conditions on the strength and durability of air entrained concrete with and without fly ash[J]. Clean Mater, 2023, 7:100170.
[94]MIRBOD M, SHOAR M. Intelligent concrete surface cracks detection using computer vision, pattern recognition, and artificial neural networks[J]. Procedia Comput Sci, 2023, 217:52–61.
[95]TAN Y, LI S L, LIU H L, et al. Automatic inspection data collection of building surface based on BIM and UAV[J]. Autom Constr, 2021, 131:103881.
[96]METZ L, Poole B, Pfau D, et al. Unrolled generative adversarial networks[J]. arXiv, 2016:1611.02163.
[97]TAN S, Shen Y, Zhou B. Improving the fairness of deep generative models without retraining[J]. arXiv, 2020:2012.04842.
[98]HEUSEL M, RAMSAUER H, UNTERTHINER T, et al. GANs trained by a two time-scale update rule converge to a local Nash equilibrium[C]//Neural Information Processing Systems, 2017
[99]CASCO-RODRIGUEZ J, ALEMOHAMMAD S, LUZI L, et al.Self-consuming generative models go MAD[C]//International Conference on Learning Representations(ICLR 2024), Vienna, Austria,2024.
[100]KAMATH M V, PRASHANTH S, KUMAR M, et al. Machinelearning-algorithm to predict the high-performance concrete compressive strength using multiple data[J]. J Eng Des Technol, 2024, 22(2):532–560.
[101]VENKETESWARAN A, LALAM N, WUENSCHELL J, et al. Recent advances in machine learning for fiber optic sensor applications[J].Adv Intell Syst, 2022, 4:2100067.
[102]GUO P W, MENG W N, XU M F, et al. Predicting mechanical properties of high-performance fiber-reinforced cementitious composites by integrating micromechanics and machine learning[J].Materials, 2021, 14(12):3143.
[103]FENG D C, WANG W J, MANGALATHU S, et al. Condition assessment of highway bridges using textual data and natural language processing-(NLP-)based machine learning models[J]. Struct Contr Health Monit, 2023, 2023:9761154.
[104]BANGARU S S, WANG C, HASSAN M, et al. Estimation of the degree of hydration of concrete through automated machine learning based microstructure analysis–A study on effect of image magnification[J]. Adv Eng Inform, 2019, 42:100975.
[105]WUDIL Y S, SHALABI A F, AL-OSTA M A, et al. Effective corrosion detection in reinforced concrete via laser-induced breakdown spectroscopy and machine learning[J]. Mater Today Commun, 2024, 41:111005.
[106]YU A P, LIU T, MIAO T J, et al. Signal recognition and prediction of water-bearing concrete under axial compression using acoustic emission and machine learning[J]. Struct Contr Health Monit, 2025,2025:6633988.
[107]HEMALATHA T, RAMASWAMY A. A review on fly ash characteristics–Towards promoting high volume utilization in developing sustainable concrete[J]. J Clean Prod, 2017, 147:546–559.
[108]VISHWAKARMA V, RAMACHANDRAN D. Green Concrete mix using solid waste and nanoparticles as alternatives–A review[J].Constr Build Mater, 2018, 162:96–103.
[109]MOGHADDAS S A, MENG W N, BAO Y. Explainable data-driven formulation of chloride migration coefficient of eco-friendly concrete based on advanced automatic programming[J]. Eng Appl Artif Intell,2025, 160:111965.
[110]MOGHADDAS S A, BAO Y. Explainable machine learning framework for predicting concrete abrasion depth[J]. Case Stud Constr Mater,2025, 22:e04686.
[111]CHENG B Y, BAO Y, MENG W N, et al. Particle packing theory-guided multi-fidelity deep learning for discovering low-carbon cost-effective high-performance concrete[J]. Appl Soft Comput, 2025,183:113678.
[112]LAMPINEN J, VEHTARI A. Bayesian approach for neural networks:Review and case studies[J]. Neural Netw, 2001, 14(3):257–274.
[113]PADMAPOORANI P, SENTHILKUMAR S, MOHANRAJ R.Machine learning techniques for structural health monitoring of concrete structures:A systematic review[J]. Iran J Sci Technol Trans Civ Eng, 2023, 47(4):1919–1931.
[114]MITCHELL M, WU S, ZALDIVAR A, et al. Model cards for model reporting[C]//Proceedings of the Conference on Fairness, Accountability,and Transparency. Atlanta GA USA. ACM, 2019:220–229.
[115]BLACKLAWS C. Algorithms:Transparency and accountability[J].Philos Trans A Math Phys Eng Sci, 2018, 376(2128):20170351.
[116]GUO P W, JIANG Z, MENG W N, et al. Multi-agent collaboration for knowledge-guided data-driven design of ultra-high-performance concrete(UHPC)incorporating solid wastes[J]. Cem Concr Compos,2025, 164:106230.
[117]WANG Z X, WU J M, SU L, et al. Optimization of ultra-high performance concrete based on response surface methodology and NSGA-II[J]. Materials, 2024, 17(19):4885.
[118]HASHMI A F, AYAZ M, BILAL A, et al. ANN modeling of mechanical properties in high-volume fly ash concrete:Multi-objective cost optimization using NSGA-II for sustainable construction[J]. Asian J Civ Eng, 2024, 25(3):2867–2882.
[119]NABIZADEH RAFSANJANI H, NABIZADEH A H. Towards human-centered artificial intelligence(AI)in architecture, engineering,and construction(AEC)industry[J]. Comput Hum Behav Rep, 2023,11:100319.
[120]JAYATHILAKAGE R, RAJEEV P, SANJAYAN J. Rheometry for concrete 3D printing:A review and an experimental comparison[J].Buildings, 2022, 12(8):1190.
[121]CIABURRO G, IANNACE G. Machine-learning-based methods for acoustic emission testing:A review[J]. Appl Sci, 2022, 12(20):10476.
[122]PARK M J, KIM J, JEONG S, et al. Machine learning-based concrete crack depth prediction using thermal images taken under daylight conditions[J]. Remote Sens, 2022, 14(9):2151.
[123]QUAH T K N, TAY Y W D, LIM J H, et al. Concrete 3D printing:Process parameters for process control, monitoring and diagnosis in automation and construction[J]. Mathematics, 2023, 11(6):1499.
[124]BADO M F, TONELLI D, POLI F, et al. Digital twin for civil engineering systems:An exploratory review for distributed sensing updating[J]. Sensors, 2022, 22(9):3168.
[125]KJELLMARK G, HJELSETH E, PEÑALOZA G A, et al. Toward construction 5.0:Bridging AI and people through continuous learning[J]. J Constr Eng Manage, 2026, 152:04025227.
基本信息:
DOI：10.14062/j.issn.0454-5648.20250765
中图分类号:TP18;TU528
引用信息:
[1]姜占,滕乐,童昕阳,等.人工智能驱动可持续混凝土：方法、机遇、挑战[J].硅酸盐学报,2026,54(03):1016-1031.DOI:10.14062/j.issn.0454-5648.20250765.
基金信息:
美国国家科学基金会项目(2046407); 美国科学院项目(SCON-10001241)
2026-02-11
2026-02-11
2026-02-11