...Md Zia Ul Haq; Sandeep Singh; Meena Y.R.; Vanitha S.; Priyadarshi Das; Manni Sharma; Tarak Vora This paper evaluates variations in supplementary cementitious materials, water-cement ratios, curing times, aggregate sizes, and cement content, the predictive efficacy of five machine learning models...

..., as they consider implementing rating systems involves perceived costs. Therefore, to predict whether construction stakeholders are likely to adopt GRS, a machine learning ( ML )-based approach is used in this study. This study used two different approaches. At first, ML algorithms – logistic regression ( LR...

... machine learning quality control rate determination UN SDG 8: Decent work and economic growth National Natural Science Foundation of China 72161007 Funding was provided by the National Natural Science Foundation of China (grant number 72161007). Inherent defects insurance (IDI...

... remains competitive in multi-keyed configurations under confining stress. To address the complexity of predicting RAC’s load-bearing capacity, this study introduces a novel predictive model developed through advanced machine learning techniques, including Linear Regression and Random Forest algorithms...