Research progress on mixture optimization of concrete based on machine learning and metaheuristic algorithms
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摘要: 混凝土配合比决定其成本、工作性能、力学性能和耐久性能，传统混凝土配合比优化方法是通过大量实验室试验，需要消耗大量时间、人力和资源。为了解决上述问题，利用机器学习和元启发式优化算法进行混凝土配合比优化已被证明是一种具有广阔前景的技术手段。本文全面回顾了有关混凝土配合比设计和优化方面的研究。首先，讨论了常用的机器学习和元启发式算法的基本工作原理和优势。然后，归纳总结了基于机器学习和元启发式算法在单一目标、多目标优化各种类型混凝土配合比方面的应用。最后，结合当前的技术水平，强调并讨论了推进混凝土配合比设计和优化领域的当前趋势和机遇，为机器学习技术在混凝土领域更深层次的开发和应用提供了依据。Abstract: The concrete mix ratio determines its cost, workability, mechanical properties, and durability. The traditional method of concrete mix ratio optimization is through a large number of laboratory tests, which consumes a lot of time, labor, and resources. To solve the above problems, concrete proportion optimization using machine learning and meta-heuristic optimization algorithms has been proven to be a promising technical tool. Presents a comprehensive review of the research on concrete proportion design and optimization. First, the basic working principles and advantages of commonly used machine learning and meta-heuristic algorithms are discussed. Then, the applications of machine learning and meta-heuristic-based algorithms in single-objective and multi-objective optimization of various types of concrete proportions are summarized. Finally, current trends and opportunities in advancing the field of concrete proportion design and optimization are highlighted and discussed in the context of the current state of the art, providing a basis for deeper development and application of machine learning techniques in the field of concrete.
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Key words:
- concrete mixture /
- machine learning /
- meta-heuristic algorithm /
- optimization /
- prediction
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图 4 粒子群优化算法示意图
Figure 4. Schematic diagram of the particle swarm optimization algorithm
图 11 基于可解释的纤维增强超高性能混凝土逆向设计方法流程图[47]
UHPFRC—Ultrahigh performance fiber reinforced concrete; GSCV—Grid searchcross-validation; SHAP—Shapley additive explanations
Figure 11. Flowchart of the reverse design method for ultrahigh performance fiber reinforced concrete based on interpretable grid search cross validation Catboost[47]
表 1 4种元启发式优化算法对比分析
Table 1. Comparative analysis of four meta-heuristic optimization algorithms
Algorithm Author Year Inspiration Class Application area Genetic algorithm Holland and Reitman[8] 1978 The process of natural selection Evolutionary-based Power system, image reconstruction, scheduling, engineering, routing, feature selection, filtering recommender system, travelling salesman problem, electromagnetic optimization. Particle swarm optimization Eberhart and Kennedy[9] 1995 The process of bird flock Swarm-based Feature selection, job scheduling, financial time series forecast, vapor-liquid equilibrium problem, engineering, economic dispatch problem, scheduling problem, medical diagnosis. Beetle antennae search Jiang X Y,
Li S[10]2017 The process of the searching behavior of longhorn beetles Individual-based Power system, image reconstruction, feature selection, job scheduling, engineering, scheduling problem, electromagnetic optimization. Artificial bee colony Karaboga and Basturk[12] 2007 The process of honey harvesting behavior of bee colony Swarm-based Scheduling, estimation of transportation energy demend, forecasting the blast-produced ground virbration, travelling salesman problem, engineering problem, clustering. 
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表 2 应用机器学习优化普通混凝土配合比
Table 2. Optimal concrete mixture based on machine learning
Algorithms Input variables Output variables Optimization objectives Evaluation metrics Datasets Year Ref. BPNN Cement, fine/coarse aggregate, water, superplasticizer Compressive strength, slump, permeability coefficient, elastic modulus – – 117 2003 [21] ANN Weathered sand, soil, cement, water, superplasticizer Compressive strength, tangent modulus – – 10 2004 [22] ANN Cement, fly ash, fine and coarse aggregates, water, superplasticizer, reducing-water ratio of superplasticizer Strength, slump – Average sum-squared error 18 2006 [23] BPNN, GA Cement, fine/coarse aggregate, fly ash, superplasticizer Compressive strength, slump – Average relative error 150 2007 [24] NN, GA Cement, fly ash, slag, water, superplasticizer, fine and coarse aggregates, binder, water/cement, water/binder water/solid, superplasticizer/binder, fly ash/binder, slag/binder, pozzolans/binder aggregate/binder, fine aggregate/aggregate Strength, slump – RMSE 103 2007 [25] GA, ANN, BPNN Cement, fly ash, fine aggregate, coarse aggregate, admixture, water-binder ratio Slump – RMSE, MAPE, R 560 2015 [26] BBP, Regression Cement, water, silica fume, coarse aggregate, fine aggregate, superplasticizer, the maximum size of aggregate and concrete age Compressive strength – RMSE 1030 2019 [27] DNN Cement, water, fine-grained aggregate, coarse aggregate, water-cement ratio Compressive strength – MAE, MSE, RMSE 741 2021 [28] ExGBT, LGBT, GAM, KDP, Anomaly detection algorithm,
K-means algorithmWater, cement, fly ash, water-reducing admixture, air-entraining admixture, coarse/fine aggregate, fresh air Fire-induced spalling, chloride penetration, strength – Area under the ROC curve, Log loss error 10000 2022 [30] DL, GP, ANN, BO Mixture proportions, chemical composition, particle size distribution, type of specimen, curing time Compressive strength – RMSE, MAE, MAD, R2 760 2023 [31] LR, NN, BT, RF, SVM Water/cement, cement, fly ash contents, water-reducing admixture, air-entraining admixture contents, coarse/fine aggregate contents, fresh air content Compressive strength Compressive strength, cost, embodied CO2 RMSE, R2, MAPE 1031 /
9994 2019 [32] WCA, SLC, GA, ANN, SVM, Regression Age, cement, water, superplasticizer, coarse aggregate, fine aggregate, the ratios of water to binder, superplasticizer to binder, fine aggregate to total aggregate, coarse aggregate to binder Compressive strength Compressive strength, cost, CO2 emission, energy, resource consumptions MAE, R, R2, MSE, RMSE 232 2020 [34] BPNN, BAS, SVM, LR, DT, MLR, KNN, RF, NSGA-II, MOEA, MOPSO Ordinary Portland cement, silica fume, water, coarse/fine aggregate, maximum size of coarse aggregate, superplasticizer, curing age Strength Strength, cost, embodied CO2 RMSE, R 1030 2021 [11] RF, DT, SVM, BAS Cement, water, blast furnace slag, coarse/fine aggregate, superplasticizer Uniaxial compressive
strengthUniaxial compressive strength, cost RMSE, R 102 2022 [35] COP, WCP, DL Age, water, cement, fine/coarse aggregate, superplasticizer, supplementary cementing materials, weight ratios of water to binder, supplementary cementing materials to binder, coarse aggregate to binder, fine aggregate to total aggregate, superplasticizer to binder Compressive strength Compressive strength, cost, emission-CO2, energy consumption, material consumption R, MSE, MAE, R2, RMSE 1200 2023 [36] Elastic net, extremely randomized tree, Lasso/Ridge/amma regression, RF, DT, AdaBoost, GB, ExGBT, LightGBM, CatBoost, NSGA-III, C-TAEA Water, cement, mineral admixture, fine/coarse aggregate, chemical admixture, performance parameter Compressive strength,
binder intensityCompressive strength,
binder intensity, costR2, RMSE, MAE, MGD 610 2023 [37] Notes: DNN—Deep neural network; ExGBT—Extreme gradient boosted tree; LGBT—Light gradient boosted tree; GAM—Generalized additive model; KDP—Keras deep residual neural network; DL—Deep learning; GP—Gaussian processes; BO—Bayesian optimization; LR—Linear regression; BT—Boosted tree; WCA—Water cycle algorithm; SLC—Soccer league competition; MSE—Mean square error; ROC—Receiver operating characteristic; MAD—Median absolute deviation; DT—Decision tree; MLR—Multivariable linear regression; KNN—K-nearest neighbours; MOEA, MOPSO—Multi-objective particle swarm optimization; COP—Coyote optimization programming; WCP—Water cycle programming; GB—Gradient boosting; C-TAEA—Constrained-targeted adaptive evolutionary algorithm; MGD—Mean gamma deviance; GA—Genetic algorithm; NN—Neural network; BBP—Biogeography-based programming; BPNN—Back-propagation neural network; BAS—Beetle antennae search; NSGA-II—Non-dominated sorting genetic algorithm II; .
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表 3 应用机器学习优化超/高性能/高强混凝土配合比
Table 3. Optimal ultra/high performance/strength concrete mixture based on machine learning
Algorithms Input variables Output variables Optimization
objectivesEvaluation metrics Datasets Year Ref. ANN Cement, fly ash, blast furnace slag, water, superplasticizer, coarse/fine aggregate, water/cement ratio, water/cementitious ratio, superplasticizer/cementitious ratio, fly ash/cementitious ratio, slag/cementitious ratio (fly ash+ slag)/cementitious ratio Compressive strength, slump – R2 695/
1201999 [33] GA Water to binder ratio, water, fine aggregate ratio, fly ash replacement ratio, silica fume replacement ratio, air-entraining agent, superplasticizer Compressive strength, slump – R2 189 2004 [38] Elitist GA Cement, fly ash, fine/coarse aggregates, water, superplasticizer, water/cement, water/(cement + fly ash), fly ash/cement, fly ash/(cement +fly ash) Compressive strength – MSE 350 2009 [39] Harmony search algorithm, NN, GA Cement, fly ash, silica fume, fine/coarse aggregates, water, superplasticizer, fine aggregate ratio, water/binder Compressive strength, slump – Probability 189 2012 [40] GA, ESVM Cement, fly ash, blast furnace slag, water, superplasticizer, coarse/fine aggregate, age of testing Compressive strength – RMSE, MAE 1030 2014 [41] GP Cement, blast furnace slag, fly ash, water, superplasticizer, coarse/fine aggregate Strength – RMSE, Pearson correlation coefficient 453 2021 [42] SOS, LSSVM, BPNN, ESIM, SVM Fine/coarse aggregate, fly ash, silica fume, cement, blast furnace slag, water, superplasticizer, age of testing Compressive strength, slump, electrical
resistivityCompressive strength, slump, electrical
resistivity, costR, RMSE, MAPE, MAE 597/
282/
2642023 [43] BPNN, GA Cement, water/cement, silica fume, superplasticizer, fine/coarse aggregate, steel fiber content Compressive strength – – 50 2020 [44] GA, ANN Cement, silica fume, limestone powder, river sand; Water/cementitious materials ratio, superplasticizer/cementitious materials ratio Packing density Workability, packing density, compressive strength, pore structure, economy, ecology R2, RDP, RMSE, MAPE 26 2020 [45] GA, ANN, LR Cement, limestone powder, silica fume, river sand Compressive strength, packing density – R2, RDP, RMSE 80 2021 [46] AdaBoost, LightGBM, XGBoost, CatBoost, RF Cement, silica fume, quartz sand, steel fiber, superplasticizer, water, concrete age Compressive strength – RMSE, R2, MAE 400 2023 [47] RF, CART Cement, silica fume, supplementary cementing materials, quartz powder, water, superplasticizer proportion, siliceous micro sand, maximum size of the sand’s particle, water to binder, mixture's total fiber volume, total fiber reinforcement index Energy absorption capacity, strain at peak stress – R2 600 2023 [48] ANN Fly ash, silica fume, superplasticizer, water/cement, water to binder Compressive strength – – 9 2004 [49] ANN, GA Water to binder ratio, sand ratio, the amount of cement per cubic meter of concrete, the ratio of fly ash/slag/silica fume mass to cementitious materials mass Slump, compressive strength, tensile strength, elastic modulus, shrinkage, creep coefficient, cracking risk coefficient Slump, compressive strength, tensile strength, elastic modulus, shrinkage, creep coefficient, cracking risk coefficient MAE, MSE, RMSE, R2 25 2020 [50] BPNN, SVR, RT, RF, KNN, LR, MLR, PSO Cement, blast furnace slag, fly ash, water, fine/coarse aggregate, bentonite, silty clay, superplasticizer, curing age Strength, slump Strength, slump, cost RMSE, R 1030 2020 [51] RF, NSGA-II Water to binder, cement, fine/coarse aggregate, fly ash,
silica fume, superplasticizerCompressive strength, chloride ion permeability coefficient Compressive strength, chloride ion permeability coefficient, cost RMSE, R2 71 2022 [52] RF, AEPSO Cement, blast furnace slag, fly ash, water, fine/coarse aggregate, superplasticizer, age Compressive strength Compressive strength, cost, emission-CO2 RMSE, R, MAE 1133 2022 [53] RF, NSGA-II, BPNN, SVM, GBDT Cement, fine/coarse aggregate, superplasticizer, fly ash, water-binder ratio Relative dynamic elastic modulus, chloride ion permeability coefficient, cost Relative dynamic elastic modulus, chloride ion permeability coefficient, cost RMSE, R2, MAPE 92 2023 [54] RF, RFE, BO, LSSVM, NSGA-III, BPNN, WNN Fly ash, cement, fine/coarse aggregate, water, composite superplasticizer, water-binder ratio, average particle size, sand ratio, mud, needle-like particles, water absorption Chloride ion permeability coefficient, relative dynamic elastic modulus, strength Chloride ion permeability coefficient, relative dynamic elastic modulus, strength, cost RMSE, R2 100 2023 [55] MLR, MLPNN, GPR, PSO Cement, silica fume, quartz flour, siliceous sand, water, polycarboxylate ether-based superplasticizers, steel microfibers Strength, slump Strength, slump, cost RMSE, R, MAE 53 2022 [56] GP, BBO, LR, RF, XGBoost, GA-ANN Cement, water, sand, admixture, quartz powder, steel fiber, silica fume, fly ash Compressive strength Compressive strength, workability, embodied-CO2, packing density, cost MSE, R2 110 2022 [57] Ridge, MLP, SVM, partial least squares, RF, BO, Light GBM, AGE-MOEA, XGBoost, extra trees, C-TAEA, NSGA-II, UNSGA-III Portland cement, fly ash, slag, silica fume, metakaolin, nano silica, limestone powder, quartz powder, fine sand, tap water, superplasticizer, steel fiber Compressive strength, flexural strength, mini-slump spread, porosity Compressive strength, flexural strength, mini-slump spread, porosity, life-cycle carbon footprint, embodied energy, material cost MAE, MAPE, MAD, R2 785 2023 [58] XGBoost, SVR, GBR, RFR, GA, NSGA-II Cement, fly ash, blast furnace slag, superplasticizer, water, coarse/fine aggregate, age Compressive strength Compressive strength, cost, CO2 emission R2, RMSE, MAE 1342 +1133 2023 [59] Notes: ESVM—Evolutionary support vector machine; SOS—Symbiotic organism search; LSSVM—Least squares support vector machine; ESIM—Evolutionary support vector machine inference model; CART—Classification and regression tree; RT—Regression tree; AEPSO—Adaptive evolutionary particle swarm optimization; GBDT—Gradient boosting decision tree; RFE—Recursive feature elimination; WNN—Wavelet neural network; MLPNN—Multi-layer perceptron neural network; GPR—Gaussian processing regression; BBO—Batch bayesian optimization; MLP—Multilayer perceptron; AGE-MOEA—Adaptive geometry estimation-based many-objective evolutionary algorithm; GBR—Gradient boosting regression; RFR—Random forest regression; RDP—Relative percent deviation.
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表 4 应用机器学习优化再生骨料/砖骨料混凝土配合比
Table 4. Optimal recycled aggregate/brick aggregate concrete mixture based on machine learning
Algorithms Input variables Output variables Optimization
objectivesEvaluation metrics Datasets Year Ref. GA Water/binder, water, cement, sand, aggregate, admixture Slump, compressive strength, final setting, specific gravity, elastic modulus, carbonation speed coefficient, price, CO2 emission Slump, compressive strength, final setting, specific gravity, elastic modulus, carbonation speed coefficient, price, CO2 emission Probability 6/4 2013 [60] NN Cement, water/cement, crushed tile, crushed brick, natural aggregate Compressive strength – MAE, RMSE, MAPE, R, E 147 2017 [61] ICA, MLP, GA, PSO Water, cement, recycled coarse aggregates, natural coarse/fine aggregates, water/cement Compressive, tensile, flexural strengths, slump – MAE 1348 2021 [62] GBRT, PSO, RNN, GP Water/cement, cement, sand, recycled aggregate, gavel, superplasticizer, silica fume, age, specimen type Compressive strength Compressive strength, cost, CO2 emission RMSE, MAE, R2 1134 2020 [63] FA, RF, BPNN, SVM Cement, water, sand, natural coarse aggregates, superplasticizer, recycled coarse aggregates, maximum size, water absorption, density Uniaxial compressive strength, splitting tensile strength Uniaxial compressive strength, splitting tensile strength, cost, CO2 emission RMSE, R 344 2020 [64] BPNN, GWO, SVM, RF, ELM, XGBoost, GRNN, PSO Cement, water/cement, crushed tile ratio, crushed brick ratio, natural aggregate ratio Compressive strength Compressive strength, cost,
CO2 emissionR, R2, SD, RMSE 182 2023 [65] BPNN, GPR, RF, BO, GBDT, NSGA-II Cement，water, sand, fly ash, aggregate replacement ratio, aggregate water absorption, age, carbon dioxide volume fraction, ambient temperature, humidity, carbonation time Electrical flux, carbonization depth Electrical flux, carbonation depth, cost, strength R2, RMSE 200+500 2023 [66] BPNN, GPR, CART, RF, BO, GBDT, XGBoost, CMOPSO Water, cement, sand, coarse aggregates, fly ash, strength grade of cement, replacement ratio of recycled coarse aggregates, weighted water absorption of coarse aggregates, maximum particle size of coarse aggregates, curing time Compressive strength Compressive strength, materials cost, carbon footprint, energy intensity R2, RMSE, MAPE 1373 2023 [67] Notes: E—Error; ICA—Imperialist competitive algorithm; GBRT—Gradient boosting regression tree; RNN—Recurrent neural network; FA—Firefly algorithm; GWO—Gray wolf optimizer; ELM—Extreme learning machine; GRNN—Generalized regression neural network; CMOPSO— Competitive mechanism-based multi-objective particle swarm optimization; SD—Standard deviation.
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表 5 应用机器学习优化地聚物/碱激发混凝土配合比
Table 5. Optimal geopolymer/alkali activated concrete mixture based on machine learning
Algorithms Input variables Output variables Optimization
objectivesEvaluation metrics Datasets Year Ref. MARS Fly ash, aggregates, alkaline activator, added water, NaOH molarity, solid% in Na2SiO3, heat curing Compressive strength – R, RMSE, MAE 98 2018 [68] ANN Fly ash, coarse aggregate, fine aggregate, NaOH activator, Na2SiO3 activator, added water, SiO2% in Na2SiO3, Na2O% in Na2SiO3, NaOH molarity, heat curing Compressive strength, flexural strength, elastic modulus – MSE, R 166 2021 [69] RF Precursor, ground granulated blast furnace slag ratio, Na2O, elastic modulus, water, fine/coarse aggregate Compressive strength, slump, static/dynamic yield stress, plastic viscosity Compressive strength, slump, static/dynamic yield stress, plastic viscosity, CO2 emission R2, MAE, MAPE, RMSE 145+193 2023 [70] ANN, GA Total mass of SiO2, Al2O3, Fe2O3, CaO, Na2O, fine/coarse aggregates, superplasticizer, water, age, curing temperature, time, relative humidity Compressive strength, slump Transportation, CO2 emission, cost RMSE, MAE, R2, R 1178 2022 [71] NSGA-II, BPNN, XGBoost Fly ash chemical composition, superplasticizer, fly ash, fine/coarse aggregate, extra water, high temperature, high temperature curing, standard curing Uniaxial compressive strength Uniaxial compressive strength, cost,
CO2 emissionMSE, MAE, R2 896 2023 [72] RFR, ETR, GBR, XGBR, BO, NSGA-II Fly ash, blast furnace slag, sodium silicate, sodium hydroxide, water, coarse/fine aggregate, curing relative humidity, temperature, curing age, silica modulus, reactivity modulus, alumina modulus, lime modulus, hydraulic modulus Uniaxial compressive strength Uniaxial compressive strength, cost,
CO2 emissionR, MAE, RMSE 676 2023 [73] RF, GB, BPNN, GPR, PSO Slag, fly ash, coarse/fine aggregate, NaOH, sodium silicate, water, superplasticizer Compressive strength Compressive strength, cost, carbon emission R2, RMSE, MAPE 143 2023 [74] Notes: RFR—Random forest regression; ETR—Extremely randomized tree; XGBR—Extreme gradient boosting regression.
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表 6 应用机器学习优化轻骨料混凝土配合比
Table 6. Optimal lightweight aggregate concrete mixture based on machine learning
Algorithms Input variables Output variables Optimization objectives Evaluation metrics Datasets Year Ref. SVR, MLP, GBR, XGBoost Cement, supplementary cementitious materials, light weight aggregates types, normal weight aggregates, superplasticizer, age of testing, oven dry densities of each light weight high strength concrete mix Compressive strength, splitting tensile strength – R2, RMSE, MAE, MAPE 403 2023 [75] SVM, ANN, DT, GPR, XGBoost Cement, sand, water-to-cement ratio, lightweight aggregate, normal aggregate, density of lightweight aggregate, water absorption of lightweight aggregate, superplasticizer, curing time, fly ash, lightweight aggregate type Compressive strength – RMSE, MAE, MSE, R2 420 2023 [76] ANN, DNN, RBM, DBN, GA, NSGA-II The percentage of lightweight expanded clay aggregate,
water-to cement ratio, cement content, silica fume dosageCompressive, tensile strength Compressive strength, cost, life cycle assessment R2, MAE, RMSE 240 2021 [77] MOFA, SVR Cement, water, natural fine aggregate, lightweight coarse aggregate, superplasticizer, the density of lightweight coarse aggregate, maximum size of coarse aggregate, age Uniaxial compressive strength, density Uniaxial compressive strength, density, cost RMSE, MAE, MAPE, R 144 2021 [78] Notes: RBM—Restricted Boltzmann machine; MOFA—Multi-objective firefly algorithm.
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表 7 应用机器学习优化其他类型混凝土配合比
Table 7. Optimal other types concrete mixture based on machine learning
Concrete types Algorithms Input variables Output variables Optimization
objectivesEvaluation metrics Datasets Year Ref. Pozzolanic concrete ANN, BPNN Water, cement, ground granulated blast furnace slag, fly ash, coarse/fine aggregate, superplasticizer Compressive strength, slump – R2, SSE 482/
2952018 [79] Cemented paste backfill PSO, MR Cement percentage, solid content, water contents Strength, yield stress, cost Strength, yield stress, cost R2 16 2020 [80] Wet-sprayed concrete ANN, PSO, GA Cement, water-cement ratio, water-reducing agent, sand/gravel ratio, age of maintenance Compressive strength – R2, MRE 32 2021 [81] Cement grouting material AdaBoost Water-cement ratio, expansion agent, water reducing agent, accelerating agent Flexural, compressive strengths, fluidity, shrinkage rate – R2 16 2022 [82] Steelmaking slag concrete SVR, ANN, XGBoost, GPR Cement, fine steel slag, coarse steel slag, fine/coarse aggregate; water, superplasticizer, pozzolanic admixtures, supplementary cementing materials, filler Compressive strength – R2, MAE, RMSE 406/
1462022 [83] Cement-based grouting materials SVR, DT, RF, AdaBoost, XGBoost, BO Water-binder ratio, sand-binder ratio, silica fume, blast furnace slag, metakaolin, fly ash, hardening expansion agent, superplasticizer, plastic expansion agent, stabilizer, anti-foaming agent, early strength agent Compressive strength, fluidity – R2, RMSE 442/
2172023 [84] Aerogel-incorporated concrete DT, SVR, MLP, RF, XGBoost, GBDT, LightGBM Water-binder ratio, aerogel replacement rate, silica fume replacement rate, age, dry/saturated state Compressive/
flexural strengths, thermal conductivity– R2, MAE, RMSE, MAPE 660 2023 [85] Pervious concrete SVR, BAS Water-to-cement, coarse aggregate size, aggregate-to-cement ratio Permeability coefficient, uniaxial compressive strength, splitting tensile strength – R, RMSE 90 2020 [86] Steel fibre reinforced concrete SVR, FA Cement, coarse/fine aggregate, superplasticiser, the volume fraction of steel fibre, the length, diameter of steel fibre Uniaxial compressive strength, flexural strength Uniaxial compressive strength, flexural strength, cost R, MAE, RMSE,
MAPE299/
2692020 [87] Strain-hardening cementitious composites AdaBoost, SVM, XGBoost, NSGA-III, UNSGA-III, ANN, CART Cement/fly ash/slag/rice husk/limestone/metakaolin/silica fume/sand/water -to-binder ratio, superplasticizer, fiber volume/length/
diameter, fiber Young's modulusCompressive strength, tensile strength, ductility Compressive strength, tensile strength, ductility, cost, CO2 emission R2, ME, MAE, MSE, MAD 745 2021 [88] Rubbercrete M5 P, MGEP, GWO Cement, water, coarse/fine aggregate, silica fume, superplasticizer, waste coarse rubber, waste fine rubber, concrete age Compressive strength Compressive strength, cost, CO2 emission, the amount waste rubber of consumption R, MAE, RMSE 712 2021 [89] Electrically conductive cementitious composites SVR, BAS Age, cement, graphite powder, ground granulated blast furnace slag, steel slag Flexural strength, electrical conductivity Flexural strength, electrical conductivity, cost R, MAPE, MAE, RMSE 252/
3362021 [90] Graphite-based nanomaterials reinforced cementitious composites KNN, SVR, RFR, XGBoost, BPNN, BO, NSGA-II Cement, sand fitness modulus, sand, graphite-based nanomaterials material/diameter/
thickness/surface area of
graphite-based nanomaterials, water, superplasticizer, ultrasonication, curing age, dryUniaxial compressive strength, electrical resistivity Uniaxial compressive strength, electrical resistivity R2, MAE, MSE 379/416 2022 [4] Green concrete MLR, KNN, SVM, GP, RF, ANN, GBM, XGBM Waste granite powder, waste marble powder, ground granulated blast furnace slag, fly ash, silica fume, cement, water, fine/coarse aggregates, superplasticizer, age Compressive strength Compressive strength, cost, global warming potential, acidification potential, fossil
fuel depletion potentialMSE, MAPE, MBE, RMSE, MAE, R2 2644 2023 [91] Notes: MR—Multiple regression; GBM—Gradient boosting machine; XGBM—Extreme gradient boosting machine; SSE—Sum of the squares error; MRE—Mean relative error; ME—Maximum error; MBE—Mean bias error.
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