This study proposes an explainable artificial intelligence (AI)-guided framework for predicting compressive strength (CS) and assessing the sustainability of ultra-high-performance concrete (UHPC) mixtures incorporating supplementary cementitious materials (SCMs), nano-additives, and sustainable waste-derived constituents. A database containing 1990 experimental records of 28-day CS was compiled from the literature. Three tree-based machine learning algorithms, namely Extra Trees Regressor (ETR), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM), together with stacked ensemble models, were developed and optimized using the Whale Optimization Algorithm (WOA). Based on outcomes, among all developed models, the SM-4 (XGB+ETR+LGBM) model achieved the best overall testing performance with an R² of 0.885, RMSE of 12.40 MPa, MAE of 8.80 MPa, MAPE of 5.94%, and RMSRE of 8.37%. SHAP analyses identified curing temperature, fiber dosage, silica fume content, cement dosage, and water content as the most influential predictors. Cradle-to-gate life cycle assessment (LCA)-based analyses further evaluated carbon emissions, embodied energy, and material cost. Additionally, a multi-objective inverse-design framework coupled the best-performing stacked model with NSGA-II and TOPSIS to identify UHPC mixture proportions for eight CS targets from 130 to 200 MPa while considering sustainability indicators and mixture-design requirements. Selected mixtures were produced and experimentally tested to compare measured CS with predictions and prescribed targets. Finally, an AI-guided graphical user interface (GUI) was developed for rapid CS prediction and sustainability assessment. The proposed framework integrates interpretable prediction, sustainability assessment, and multi-objective optimization to identify UHPC mixtures for prescribed strength targets, with experimental validation supporting practical mixture-design applications.
All-solid-state batteries (ASSBs) offer improved safety and energy density compared to conventional liquid–electrolyte systems, yet their widespread adoption is hindered by complex electro-chemo-mechanical degradation mechanisms. In this work, we introduce a stochastic continuum framework to capture microstructural heterogeneity and its influence on interfacial degradation in ASSBs. The approach combines physics-based modeling of electro-chemo-mechanical coupling, including a traction–separation law for interface decohesion, with non-Gaussian, spatially correlated random fields representing the variability of solid electrolyte properties. Random fields are constructed via a Gaussian translation method with Gamma marginals and a copula-based correlation structure, enabling realistic representation of particle-scale property fluctuations while maintaining computational efficiency. Numerical simulations on a representative half-cell of NMC811 cathode particles embedded in LPSCl electrolyte demonstrate the emergence of localized stress concentrations and interface damage under cyclic charging. The model quantitatively captures the evolution of specific differential capacity and depth of discharge, revealing how heterogeneous electrolyte properties amplify interfacial failure and drive catastrophic degradation. This framework provides a versatile tool for predictive design and reliability assessment of ASSBs accounting for microstructural variability.
For gravity dams constructed on soft rock, displacements at the same reservoir level may differ between filling and drawdown. Conventional inversion frameworks use the current reservoir level as the hydraulic input but neglect the loading path. Cyclic reservoir effects may therefore be misattributed to foundation creep, biasing the identified viscoelastic parameters. A reservoir-path-informed inverse framework is proposed to identify Burgers parameters from displacements at multiple monitoring points. First, an adaptive-window descriptor encodes reservoir-operation history using local water-level change, phase duration, and loading or unloading trend. Second, a physics-guided multi-output temporal convolutional network with factor attention (PG-ATCN) replaces repeated finite-element analyses during inversion. Separate branches encode viscoelastic-parameter, hydraulic-loading, creep-time, and reservoir-path information. Factor attention provides relative weights for selected physical factors, while a correlation-based output constraint preserves inter-point dependence and point-specific responses. Third, an adaptive multi-objective grey wolf optimizer (A-MOGWO) treats pointwise fitting errors as separate objectives, avoiding prescribed sensor weights. Archive-feedback operator selection, density-aware archive maintenance, and stagnation-triggered local refinement improve convergence, Pareto coverage, and error balance. Benchmarks against MOGWO and NSGA-II demonstrate A-MOGWO's performance. For a monitored roller-compacted concrete gravity dam, PG-ATCN reduced test-set RMSE by 22.8–35.5% relative to the best-performing baseline. Ablation analyses confirmed the contribution of reservoir-path information, and the identified parameters reproduced calibration and validation displacements at all monitoring points. The framework provides an approach to multi-point viscoelastic parameter identification in dam-foundation systems with path-dependent responses.
Artificial ground freezing (AGF) requires numerical simulation frameworks capable of representing phase-transition-induced soil-property evolution while maintaining computational efficiency for field-scale applications. This study presents a machine-learning-informed sequential thermo-mechanical simulation framework for AGF that incorporates soil freezing characteristic curves (SFCCs), continuous temperature-dependent property updating, and staged construction analysis within a unified computational workflow. A pretrained extreme gradient boosting–based pedotransfer function (XGB-PTF) was used prior to the FLAC3D simulation to generate soil-specific SFCCs from routinely available soil properties. The resulting SFCCs were then used to continuously update thermal conductivity and specific heat capacity as functions of temperature during the sequential thermo-mechanical analysis. The proposed framework was evaluated against field measurements from an AGF-assisted deep excavation project and benchmarked against a conventional stepwise property-switching approach. Throughout the 500-day simulation, the maximum temperature difference between consecutive update cycles remained below 0.5 °C, demonstrating stable sequential updating. Compared with the benchmark model, the proposed framework showed closer agreement with the measured ground-temperature histories and diaphragm-wall deformation, with a final-stage wall-deflection RMSE of approximately 6 mm, compared with more than 10 mm for the benchmark model. These results indicate that XGB-PTF-predicted SFCCs can provide practical soil-specific inputs for continuous temperature-dependent property updating in field-scale sequential thermo-mechanical AGF simulations.
