This paper presents a novel application of machine learning models to clarify the intricate behaviors of expansive soils, focusing on the impact of sand content, saturation level, and dry density. Departing from conventional methods, this research utilizes a data-centric approach, employing a suite of sophisticated machine learning models to predict soil properties with remarkable precision. The inclusion of a 30% sand mixture is identified as a critical threshold for optimizing soil strength and stiffness, a finding that underscores the transformative potential of sand amendment in soil engineering. In a significant advancement, the study benchmarks the predictive power of several models including extreme gradient boosting (XGBoost), gradient boosting regression (GBR), random forest regression (RFR), decision tree regression (DTR), support vector regression (SVR), symbolic regression (SR), and artificial neural networks (ANNs and proposed ANN-GMDH). Symbolic regression equations have been developed to predict the elasticity modulus and unconfined compressive strength of the investigated expansive soil. Despite the complex behaviors of expansive soil, the trained models allow for optimally predicting the values of unconfined compressive parameters. As a result, this paper provides for the first time a reliable and simply applicable approach for estimating the unconfined compressive parameters of expansive soils. The proposed ANN-GMDH model emerges as the pre-eminent model, demonstrating exceptional accuracy with the best metrics. These results not only highlight the ANN’s superior performance but also mark this study as a groundbreaking endeavor in the application of machine learning to soil behavior prediction, setting a new benchmark in the field.

This study aims to evaluate the utilization of technology known as Digital Twin for fault detection in buildings. The strategy consisted of studying existing applications, difficulties, and possibilities that come with it. The Digital Twin technology is one of the most intriguing newly discovered technologies rapidly evolving; however, some problems still need to be addressed. First, using Digital Twins to detect building faults to prevent future failures and cutting overall costs by improving building maintenance is still ambiguous. Second, how Digital Twin technology may be applied to discover inefficiencies inside the building to optimize energy usage is not well defined. To address these issues, we reviewed 326 documents related to Digital Twin, BIM, and fault detection in civil engineering. Then out of the 326 documents, we reviewed 115 documents related to Digital Twin for fault detection in detail. This study used a qualitative assessment to uncover Digital Twin technology’s full fault detection capabilities. Our research concludes that Digital Twins need more development in areas such as scanner hardware and software, detection and prediction algorithms, modeling, and twinning programs before they will be convincing enough for fault detection and prediction. In addition, more building owners, architects, and engineers need substantial financial incentives to invest in condition monitoring before many of the strategies discussed in the reviewed papers will be used in the construction industry. For future investigation, more research needs to be devoted to exploring how machine learning may be integrated with other Digital Twin components to develop new fault detection methods.

Efficient and reliable design of Geosynthetic Reinforced Pile-Supported (GRPS) embankments on soft ground remains challenging due to the complex nonlinear soil–structure interaction and the high computational cost of repeated numerical analyses and optimization procedures. This study presents a hybrid machine-learning-based multi-objective optimization framework for the design and performance enhancement of geosynthetic reinforced pile-supported (GRPS) embankments. A comprehensive database comprising 4, 282 validated 3D numerical simulations was developed using PLAXIS 3D to capture nonlinear interactions between key design and geotechnical parameters. Multiple machine-learning algorithms, including Decision Tree, Random Forest Regression (RFR), Gradient Boosting, XGBoost, and LightGBM, were evaluated. RFR was retained as the most stable overall multi-output surrogate model, with test-set determination coefficients of 0.996 for load efficiency, 0.941 for geosynthetic tension, 0.983 for maximum settlement, 0.973 for differential settlement, and 0.983 for consolidation time. The database is described as a pairwise-informed augmented parametric database rather than a standard balanced orthogonal array. Feature-importance analysis identified embankment height, soft-soil stiffness, pile spacing, pile-cap width, and embankment friction angle as the dominant variables influencing GRPS embankment performance. These variables were used to structure the subsequent optimization stage. The NSGA-II procedure was implemented as a conditional pile-spacing optimization for each fixed combination of soft-soil stiffness, embankment height, pile-cap width, and friction angle, while less influential variables were held constant. The optimization simultaneously minimized the deviation from a target settlement of 5 cm, minimized consolidation time, and maximized load efficiency under serviceability and constructability constraints. The Ideal Point Method was applied using normalized objectives and Euclidean distance to identify the most balanced feasible solution on each Pareto front. Residual diagnostics, convergence assessment, and surrogate-uncertainty sensitivity checks were included to support the interpretation of the optimized design recommendations. The results demonstrate that the proposed ML-NSGA-II framework provides a computationally efficient and physically interpretable approach for identifying practical GRPS embankment configurations with controlled settlement, reduced consolidation time, and enhanced load-transfer efficiency within realistic construction limits.