Affiliations: [a]
School of Management, Shaanxi Institute of International Trade & Commerce, Xi’an, Shaanxi, China
| [b]
Department of Science and Technology Information, China Railway First Group Co., Ltd., Xi’an, Shaanxi, China
Correspondence:
[*]
Corresponding author. Hongling Zhang, School of Management, Shaanxi Institute of International Trade & Commerce, Xi’an 710000, Shaanxi, China. E-mail: [email protected].
Abstract: The qualities of the materials employed to manufacture concrete are significantly impacted by high temperatures, which results in a noticeable decrease in the material’s strength characteristics. Concrete must be worked very hard and allowed to reach the required compressive strength (fc). Nevertheless, a preliminary estimation of the desired outcome may be made with an outstanding degree of reliability by using supervised machine learning algorithms. The study combined the Dingo optimization algorithm (DOA), Coot bird optimization (COA), and Artificial rabbit optimization (ARO) with Random Forests (RF) evaluation to determine the fc of concrete at high temperatures. The abbreviations used for the combined methods are RFD, RFC, and RFA, respectively. Remarkably, removing the temperature (T) parameter from the input set leads to a remarkable 1100% improvement in the effectiveness index (PI) and normalized root mean squared error (NRMSE), while causing a significant fall in the coefficient of determination (R2). The findings suggest that all RFD, RFC, and RFA have substantial promise in properly forecasting the fc of concrete at high temperatures. More precisely, the RFD algorithm demonstrated exceptional precision with R2 values of 0.9885 and 0.9873 throughout the training and testing stages, respectively. Through a comparison of the error percentages for RFD, RFC, and RFA in error-based measurements, it becomes evident that RFD exhibits an error rate that is about 50% smaller compared to that of RFC and RFA. This prediction is crucial for various industries and applications where concrete structures are subjected to elevated temperatures, such as in fire resistance assessments for buildings, tunnels, bridges, and other infrastructure. By accurately forecasting the compressive strength of concrete under these conditions, engineers and designers can make informed decisions regarding the material’s suitability and performance in high-temperature environments, leading to enhanced safety, durability, and cost-effectiveness of structures.
