Machine learning based computational approach for crack width detection of self-healing concrete. (December 2022)

• Record Type: Journal Article
• Authors: Althoey, Fadi; Amin, Muhammad Nasir; Khan, Kaffayatullah; Usman, Mian Muhammad; Khan, Mohsin Ali; Javed, Muhammad Faisal; Sabri, Mohanad Muayad Sabri; Alrowais, Raid; Maglad, Ahmed M.
• Is Part Of: Case studies in construction materials. Volume 17(2022)
• Publication Date: 2022-12
• Subjects: Artificial intelligence (AI) -- Machine learning (ML) -- Gene expression programming (GEP) -- Self-healing concrete -- Cracks prevention; Building materials -- Case studies -- Periodicals; 691.05
• DOI: 10.1016/j.cscm.2022.e01610
• Languages: English
• ISSNs: 2214-5095

Abstract

Abstract: Concrete structures frequently experience the phenomena of crack development. The researchers used certain healing agents to boost the frequently observed autogenous crack-healing capacity of concrete. Although various artificial intelligence (AI) techniques have been used to forecast a number of concrete properties, the application of AI to forecast self-healing capacity of engineering cementitious composites (ECC) is relatively rare. For this purpose, three gene expression programming (GEP) models were created to predict the potential of admixture-based concrete to self-heal. A total of 619 data points were extracted from the literature with four contributing factors i.e., amount of fly ash (FA), silica fume (SF), limestone powder (LP), and crack width before self-healing (CWB) in order to forecast the crack width after self-healing (CWA). The data points were divided into training (70%) and testing (30%) sets. Various performance indicators were employed to assess the efficacy of the derived GEP models, which includes coefficient-of-correlation (R), relative-root-mean-square-error (RRMSE), root-mean-squared-error (RMSE), root-mean-squared-logarithmic-error (RMSLE), Nash-Sutcliff efficiency (NSE), root-squared-error (RSE), mean-absolute-error (MAE), performance-index (PI), and objective-function (OBF). The best optimized GEP model (SHC-GEP 1), was found with R = 0.938, NSE = 0.944, RMSE = 2.799 µm, MAE = 3.72 µm, RMSLE = 0.006 µm, RSE = 0.124 µm, and RRMSE = 0.439 µm, in the testing phase. While the OBF was less than 0.2 (i.e., 0.119), indicating that the model is free from overfitting issue. In contrary, the conventional linear regression model fails to meet this criterion and gives many negative predicted values. Moreover, the sensitivity analysis results indicate that the CWB has the greatest impact on the CWA. Also, the parametric trends between input variables and CWB, are in-line with the previous literature, indicating the robustness of the established model. Additionally, the suggested GEP model may be used as a cutting-edge alternative for forecasting the final crack width after concrete self-heals, helping engineers to assess the crack-reduction capability. Furthermore, it is recommended to explore the crack healing capabilities of other supplementary cementitious material like bagasse ash, wheat straw ash, and soda glass powder, subjected to modeling their healing ability using AI techniques. Graphical Abstract: ga1