Application of soft computing in estimating primary crack spacing of reinforced concrete structures

The investigation related to the serviceability analysis, particularly in terms of crack spacing prediction, has remarkably increased recently. In addition, the prediction of serviceability analysis is highly dependent and influenced by different physical and material factors that contribute to the crack spacing of reinforced concrete (RC) structures. As a result, the cracking phenomenon has not been fully grasped due to these factors' wide variety and complexity. Recently, soft computing techniques have gained considerable popularity due to their capability of learning and producing generalized solutions and exhibiting desirable performance in terms of time, effort, and cost. However, the literature on crack spacing prediction using various machine learning approaches is limited and insufficient. Therefore, this article is dedicated to estimating the primary crack spacing of RC structures using different machine learning methods. As a part of the study, the findings of these approaches will be computed and compared to the benchmark experimental results. Besides, the results of the developed models will be compared against that of available approaches in the literature to highlight their reliability. Furthermore, a parametric assessment will be conducted to emphasize the most influencing input parameter on the primary crack spacing of RC structures.