Reduced Beam Sections (RBS) are used in steel design to promote ductile behavior by shifting inelastic deformation away from critical joints, enhancing seismic performance through controlled energy dissipation. While current design guidelines assist in detailing RBS connections, moment–rotation curves—essential for understanding energy dissipation—require extensive testing and/or modeling. Machine learning (ML) offers a promising alternative for predicting these curves, yet few studies have explored ML-based approaches, and none, to the best of the authors’ knowledge, have applied Explainable Artificial Intelligence (XAI) to interpret model predictions. This study presents an ML framework using Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting (GB), and Ridge Regression (RR) trained on 500 numerical models to predict the moment–rotation backbone curve of RBS connections under cyclic loading. Among all the models applied, the ANN obtained the highest R² value of 99.964%, resulting in superior accuracy. Additionally, Shapley values from XAI are employed to evaluate the influence of input parameters on model predictions. The average SHAP values provide important insights into the performance of RBS connections, revealing that cross-sectional characteristics significantly influence moment capacity. In particular, flange thickness (t_f), flange width (b_f), and the parameter “c” are critical factors, as the flanges contribute the most substantially to resisting bending moments.

This study experimentally and numerically examines the structural and seismic performance of recycled solid-brick masonry infills and load-bearing walls constructed from demolition materials. Solid bricks recovered from demolished structures were reused as infill in reinforced concrete (RC) frames and as standalone walls. Five full-scale panels, bare, 50% infilled, and 100% infilled frames, were tested under diagonal compression in accordance with ASTM E519-17, simulating in-plane seismic loading. Results showed that fully infilled frames exhibited a 149% increase in diagonal shear strength but a 40% reduction in ductility relative to the bare frame, indicating a trade-off between stiffness and deformation capacity. Finite element simulations using the Concrete Damaged Plasticity (CDP) model reproduced the experimental load–displacement curves with close agreement (within 6–8% in peak load) and captured the main failure patterns. Reusing cleaned demolition bricks reduces the demand for new fired bricks and helps divert construction waste from landfill, contributing to sustainable and circular construction. The findings confirm the potential of recycled masonry for low-carbon and seismic-resilient construction, provided that ductility limitations are appropriately addressed in design.

Organized Industrial Zones (OIZs) are highly vulnerable to disasters and business continuity disruptions due to their intensive production activities, critical infrastructure connections, and the concentration of numerous businesses. However, studies that address disaster management and business continuity performance together in OIZs and quantitatively test the relationships among the variables affecting this performance are limited. This study aims to identify the key variables affecting this performance and to test the relationships among them within a holistic structural model. A conceptual framework based on the literature and a questionnaire were developed accordingly; data were collected from 81 participants with knowledge and experience in this field. The proposed model was analyzed using Structural Equation Modeling (SEM). The findings showed that management and leadership have a strong influence on mitigation and preparedness processes, and that disaster management performance can largely be shaped by response and recovery capacity. The results revealed that disaster management performance in OIZs is strengthened not only by mitigation and preparedness activities but also by transforming these activities into effective response and recovery capacity. This paper contributes to the literature by proposing an integrated model and provides practitioners with a framework for developing capacity for mitigation, preparation, coordination, response, and recovery.

Sustainable construction adoption in developing economies is often discussed in terms of financial, regulatory, and institutional barriers, while the organizational capabilities required for implementation have received less attention. This study examines the associations between key organizational and external factors and the adoption of sustainable construction practices in Jordan using partial least squares structural equation modeling (PLS-SEM) and the Technology–Organization–Environment (TOE) framework. Data were collected through a cross-sectional questionnaire completed by 192 contractors, consultants, developers, and other construction professionals. Financial Barriers, Economic Support, Regulatory Support, Human Capacity, and Technological Readiness were modeled as antecedents of adoption, while Perceived Sustainability Value (PSV) was modeled as the outcome. Human Capacity (β = 0.273, p = 0.001) and Technological Readiness (β = 0.300, p = 0.002) were positively associated with adoption, whereas Financial Barriers, Economic Support, and Regulatory Support were not statistically significant in the full model. Adoption was positively associated with PSV (β = 0.563, p < 0.001). The model accounted for 44.5% and 31.7% of the variance in adoption and PSV, respectively. The findings distinguish between barriers identified by practitioners as important and organizational capabilities most strongly associated with reported implementation. Strengthening training, technical expertise, equipment, and access to appropriate technologies may support sustainable construction adoption in Jordan. Because the data are cross-sectional and self-reported, the results are interpreted as associations rather than causal effects.