Infrastructure design, construction and development experts are making frantic efforts to overcome the overbearing effects of greenhouse gas emissions resulting from the continued dependence on the utilization of conventional cement as a construction material on our planet. The amount of CO₂ emitted during cement production, transportation to construction sites, and handling during construction activities to produce concrete is alarming. The present research work is focused on proposing intelligent models for fly ash (FA)-based concrete comprising cement, fine and coarse aggregates (FAg and CAg), FA, and water as mix constituents based on environmental impact (P) considerations in an attempt to foster healthier and greener concrete production and aid the environment. FA as a construction material is discharged as a waste material from power plants in large amounts across the world. Its utilization as a supplementary cement ensures a sustainable waste management mechanism and is beneficial for the environment too; hence, this research work is a multi-objective exercise. Intelligent models are proposed for multiple concrete mixes utilizing FA as a replacement for cement to predict 28-day concrete compressive strength and life cycle assessment (LCA) for cement with FA. The data collected show that the concrete mixes with a higher amount of FA had a lesser impact on the environment, while the environmental impact was higher for those mixes with a higher amount of cement. The models which utilized the learning abilities of ANN (-BP, -GRG, and -GA), GP and EPR showed great speed and robustness with R² performance indices (SSE) of 0.986 (5.1), 0.983 (5.8), 0.974 (7.0), 0.78 (19.1), and 0.957 (10.1) for Fc, respectively, and 0.994 (2.2), 0.999 (0.8), 0.999 (1.0), 0.999 (0.8), and 1.00 (0.4) for P, respectively. Overall, this shows that ANN-BP outclassed the rest in performance in predicting Fc, while EPR outclassed the others in predicting P. Relative importance analyses conducted on the constituent materials showed that FA had relatively good importance in the concrete mixes. However, closed-form model equations are proposed to optimize the amount of FA and cement that will provide the needed strength levels without jeopardizing the health of the environment.

A state-of-the-art review has been conducted in this work on soil constitutive modeling, which has emphasized on: soil type, ground-water conditions, loading conditions, structural behavior, constitutive relation discipline, and dimensions. By extension also, the soil constitutive applications were reviewed on the bases of: single discipline dealing with soil mechanical properties constitutive modeling which included slope stability problems, bearing capacity, settlement of foundations, earth pressure problems, soil dynamics, soil structure interaction, thermal and hydrological conditions; bi-discipline (coupled problems) which solve problems related to thermomechanical (freeze/thaw conditions), smoothed particle hydrodynamics (SPH) and hydromechanical (consolidation, collapse and liquefaction) conditions in soils and rocks and multi-discipline constitutive models which solve complex problems related to thermo-hydromechanical (THM) conditions in soils and rocks. This work has shown that smoothed particle hydrodynamics (SPH) and hydromechanical (HM) models, which belong to bi-discipline or coupled conditions are better suited for geotechnical applications, generally, while thermo-hydromechanical (THM) models, which belong to multi-discipline are better suited to solving freeze/thaw and thermal piles problems and these are proven with high performance and flexibility.

Soil-structure interaction (SSI) could affect the seismic response of structures. Since liquid storage tanks are vital structures and must continue their operation under severe earthquakes, their seismic behavior should be studied. Accordingly, the seismic response of two types of steel liquid storage tanks (namely, broad and slender, with aspect ratios of height to radius equal to 0.6 and 1.85) founded on half-space soil is scrutinized under different earthquake ground motions. For a better comparison, the six considered ground motions are classified, based on their pulse-like characteristics, into two groups, named far and near fault ground motions. To model the liquid storage tanks, the simplified mass-spring model is used and the liquid is modeled as two lumped masses known as sloshing and impulsive, and the interaction of fluid and structure is considered using two coupled springs and dashpots. The SSI effect, also, is considered using a coupled spring and dashpot. Additionally, four types of soils are used to consider a wide variety of soil properties. To this end, after deriving the equations of motion, MATLAB programming is employed to obtain the time history responses. Results show that although the SSI effect leads to a decrease in the impulsive displacement, overturning moment, and normalized base shear, the sloshing (or convective) displacement is not affected by such effects due to its long period.

The increasing population, limited residential space, and scarcity of suitable land for construction have led to a rise in the construction of high-rise buildings (HRBs) as a means to provide additional housing. The increase in human activities (such as excavations for geothermal resources) has led to an increase in seismic activity, making HRBs more vulnerable to earthquakes. Structural analysis of HRBs that are exposed to seismic loadings depends on one of the most important factors that judge the stability and resistance to seismic waves which is the stiffness of the buildings. Various methods that reduce flexibility and enhance stiffness for HRBs are the subject of this numerical investigation, and they were applied to a high-rise building (HRB) of 20 floors, considering soil–structure interaction (SSI). The stiffening methods adopted herein are steel or concrete shear walls (SWs) in specific arrangements, and different bracings in specific arrangements. Moreover, in this study, tuned mass dampers (TMDs) are used, as a damping method of the HRB seismic response, also considering SSI, and the results are compared with the stiffening methods results to judge how the applied methods enhance the seismic resistance of HRBs. These methods, which were used to influence the stiffness or the damping of the building, had different positive effects on the seismic response of the HRBs, which appeared in the results through the base shears, the base moments, the maximum top displacement, and the fundamental period of the building.

The depletion of natural sand resources and the environmental impact of cement production necessitate sustainable alternatives in concrete manufacturing. This study evaluates the potential of zeolite, ground granulated blast-furnace slag (GGBS), and construction and demolition waste (CDW) as partial replacements for sand in concrete mix proportions. Experimental investigations revealed that the optimal mix proportion, identified as Mix Batch M4 (60% Sand, 20% Zeolite, 10% GGBS, and 10% CDW), achieved a compressive strength (CS) of 67.37 MPa, flexural strength (FS) of 6.80 MPa, split tensile strength (ST) of 5.61 MPa, and notable reductions in water absorption (WA) to 4.00% and drying shrinkage (DS) to 4.02%. Additionally, durability improvements included a 30% reduction in rapid chloride permeability and enhanced ultrasonic pulse velocity (UPV) and rebound hammer (RH) values. Advanced machine learning models were utilized to analyze and optimize the mix designs, integrating the Sparrow Search Algorithm (SSA) with models such as Extreme Gradient Boosting (XGB), Backpropagation Neural Network (BPNN), Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and LightGBM. The XGB model demonstrated the highest predictive accuracy with an R² of 1.000. Multi-objective optimization techniques, including Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Genetic Algorithm with Fuzzy models, were employed to refine mix proportions further, balancing mechanical properties, material sustainability, and environmental benefits. This study highlights significant reductions in natural sand consumption and waste generation while enhancing concrete performance. Practical implications include reduced environmental impact, improved resource efficiency, and the promotion of circular economy principles. These findings provide a pathway toward innovative and sustainable concrete solutions, aligning with global sustainability goals in the construction industry.