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This research study utilizes four machine learning techniques, i.e., Multi Expression programming (MEP), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Ensemble Decision Tree Bagging (DT-Bagging) for the development of new and advanced models for prediction of Marshall Stability (MS), and Marshall Flow (MF) of asphalt mixes. A comprehensive and detailed database of 343 data points was established for both MS and MF. The predicting variables were chosen among the four most influential, and easy-to-determine parameters. The models were trained, tested, validated, and the outcomes of the newly developed models were compared with actual outcomes. The root squared error (RSE), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE), root mean square error (RMSE), relative root mean square error (RRMSE), regression coefficient (R2), and correlation coefficient (R), were all used to evaluate the performance of models. The sensitivity analysis (SA) revealed that in the case of MS, the rising order of input significance was bulk specific gravity of compacted aggregate, Gmb (38.56 %) > Percentage of Aggregates, Ps (19.84 %) > Bulk Specific Gravity of Aggregate, Gsb (19.43 %) > maximum specific gravity paving mix, Gmm (7.62 %), while in case of MF the order followed was: Ps (36.93 %) > Gsb (14.11 %) > Gmb (10.85 %) > Gmm (10.19 %). The outcomes of parametric analysis (PA) consistency of results in relation to previous research findings. The DT-Bagging model outperformed all other models with values of 0.971 and 0.980 (R), 16.88 and 0.24 (MAE), 28.27 and 0.36 (RMSE), 0.069 and 0.041 (RSE), 0.020 and 0.032 (RRMSE), 0.010 and 0.016 (PI), 0.931 and 0.959 (NSE), for MS and MF, respectively. The results of the comparison analysis showed that ANN, ANFIS, MEP, and DT-Bagging are all effective and reliable approaches for the estimation of MS and MF. The MEP-derived mathematical expressions represent the novelty of MEP and are relatively simple and reliable. Roverall values for MS and MF were in the order of DT-Bagging >MEP >ANFIS >ANN with all values exceeding the permitted range of 0.80 for both MS and MF. Hence, all the modeling approaches showed higher performance, possessed high generalization and predication capabilities, and assess the relative significance of input parameters in the prediction of MS and MF. Hence, the findings of this research study would assist in the safer, faster, and sustainable prediction of MS and MF, from the standpoint of resources and time required to perform the Marshall tests.

Engineered cementitious composite (ECC) is a special material that, when continuously hydrated, can considerably aid in self-healing. It is necessary to look at the capacity of ECC for self-healing – as measured by crack-widthafter (CWA) – when process of healing has completed, gauge the severity of the cracking, and foresee the extent of the healing. However, modeling and forecasting capacity of ECC for self-healing is a challenging task. Prediction of self-healing is a notably uncommon application of machine learning (ML), which has been applied to forecast a range of concrete properties. To estimate the capacity of ECC for self-healing, this study used three different the ensemble ML algorithms namely AdaBoost regressor (AR), decision tree (DT), and bagging regressor (BR). In addition, k-fold cross-validation method is utilized to assess the model effectiveness. With an R2 value of 0.974, the BR model was more successful in predicting outcomes when compared to the DT and AR models. Improved model performance was shown for ensemble models with smaller MAE (AR = 3.40, and BR = 1.89), MSE (AR = 27.09, and BR = 10.40), and RMSE (AR = 5.21, and BR = 3.23) values and larger R2 (AR = 0.933, and BR = 0.974) values as compared to DT (MAE = 4.29, MSE = 43.28, RMSE = 6.58, R2 = 0.894). Eventually this study will lead to savings in time, effort, and money, and the use of ML approaches to predict CWA will advance the field of civil engineering. It is also advised to investigate the crack-healing properties of additional cementitious materials such rice husk ash, wheat straw ash, and pumice powder subject to modeling their crack-healing properties using ML approaches.

There have been extensive experimental studies available on the composition and characteristics of Ultra-High-Performance concrete (UHPC). However, the relation between UHPC characteristics and mixture content, on the other hand, is extremely non-linear and challenging to distinguish utilizing typical statistical approaches. A comprehensive literature research was carried out for this aim to acquire experimental data on the compressive strength of UHPC. The dataset contains 810 experimental values of compressive strength and 15 most influential parameters that include cement, water, nano-silica, quartz powder, limestone powder, gravel, sand, slag, superplasticizer, fiber, temperature, age, fly ash, relative humidity, and silica fume, are considered as input. The suggested gene expression programming (GEP) model can estimate the compressive strength of UHPC by using simple mathematical formulations. There is no predetermined function to evaluate in the GEP technique, and it replicates or eliminates numerous combinations of factors to create the formulation that suits the experimental results. For verification and validation of model performance, various statistical measures, SHAP analysis, external validation checks, and comparing with the regression model, are applied. SHAP analysis provided that age, fiber, silica fume, superplasticizer, cement, sand, and water have a high influence on compressive strength while other input parameters have less influence on compressive strength. The model outcomes indicate the robustness and accuracy of the predictive potential of the proposed model. As a result, the GEP model can be used to give practical insights into the mixture design of UHPC for a variety of construction applications, resulting in better predictive capacity at a cheaper cost and in a considerably shorter period. Also, the present study findings can assist the design engineers and builders to understand the significance of each constituent in UHPC.