This study investigates the effects of strength and durability of concrete for different water-cement ratios, aggregate contents, and partial replacement of biomedical waste ash at 5%, 10%, 15%, 20%, and 25% by weight of cement. At 7, 14, and 28 days, the control mix showed inferior mechanical properties, particularly compressive strength, compared to concrete mixtures containing Biomedical Waste Ash (BWA). The replacement of cement by 5% and 10% increased the compressive strength but it is decreasing from 15%. Additionally, BWA modified concrete demonstrated a slower water absorption rate and minimal weight loss under acid test curing conditions, indicating enhanced durability. The economic and environmental benefits of incorporating biomedical waste into concrete promote sustainable construction practices. Using three machine learning approaches—K-Nearest Neighbors (KNN), Random Forest (RF), and CatBoost—the compressive strength of concrete with biomedical waste ash was simulated. Cement, biomedical waste, water absorption, slump, and the water-to-cement ratio were key input variables. Among the models tested, the RF model emerged as the most accurate, with a predictive performance of R² = 0.9945 and RMSE = 0.7080. Its unparalleled reliability, consistency, and accuracy in predicting compressive strength make it a top choice for this task.

There has been a significant decline in worker productivity at construction sites globally owing to the increase in accidents and fatalities due to unsafe behavior among workers. Although many studies have explored the incidence of unsafe behaviors among construction workers, limited studies have attempted to evaluate the causal factors and to determine the root causes. An integrative interpretive structural modeling analysis of the interrelationships that exist between these causal factors established from relevant literature was conducted in this study to determine the root factors hence bridging this gap. Fifteen causal factors were identified through literature review, and the nature of interrelationships between them was determined using interpretive structural modeling (ISM) and a Cross-impact matrix multiplication applied to classification (MICMAC) analysis. Data was obtained from a purposively selected cohort of experts using semi-structured interviews. The emergent data was subsequently analyzed using the ISM and MICMAC analysis to ascertain the interrelationships between the causal factors. The results of the study showed that age, sleep quality, degree of interaction and workers’ skillsets were the root causes of unsafe behavior among construction workers. Besides engendering the establishment of the root causes of unsafe behavior among construction workers, the results of this study will facilitate the prioritization of appropriate solutions for tackling the menace.

The performance of the construction industry can be improved by understanding the factors that affect the productivity of its equipment. A hypothetical framework was used to analyze six vital construction equipment parameters to understand how they affect productivity in construction projects. Data collected through a survey of 110 respondents in the construction industry were analyzed using an exploratory factor analysis (EFA) and structural equation modeling (SEM). The final model obtained using SEM consisted of 31 attributes from six construction equipment productivity factors, namely management (MG), materials (MT), human (HM), technical (TN), environmental (EM), and other factors (OT). Construction equipment productivity was found to be significantly influenced by MT and OT in construction projects. This was mainly because of their corresponding subfactors, such as operating life and equipment age, and the occurrence of accidents during construction. Consequently, based on survey feedback from various construction professionals, present gaps in construction equipment productivity were analyzed, and recommendations were made to overcome the main limiting factors under MT and OT. This study identified and quantified the interrelationships between various construction equipment productivity constraints. Therefore, the results can help experts and specialists better comprehend how to overcome delays due to idle time and improve construction equipment productivity.

Predicting the axial Shortening strength of concrete-filled steel tubular (CFST) columns is an important problem that this study attempts to solve for civil engineering projects. We suggest using a deep learning-based artificial neural network (ANN) model to address this issue, taking into account the intricate relationship between steel tube and core concrete. The model, called ANN-SFRC (Steel Fibre Reinforced Concrete), surpasses an R² threshold of 0.90 and achieves impressive R² values across different types of CFST columns. Compared to traditional linear regression methods, the ANN-SFRC model significantly improves accuracy, with an observed inaccuracy of less than 3% compared to actual values. With its reliable approach to forecasting the behavior of CFST columns under axial compression, this high-performance instrument enhances safety and accuracy during the design and planning stages of civil engineering.