This study provides the application of a machine learning-based algorithm approach names "Multi Expression Programming" (MEP) to forecast the compressive strength of carbon fiber-reinforced polymer (CFRP) confined concrete. The suggested computational Multiphysics model is based on previously reported experimental results. However, critical parameters comprise both the geometrical and mechanical properties, including the height and diameter of the specimen, the modulus of elasticity of CFRP, unconfined strength of concrete, and CFRP overall layer thickness. A detailed statistical analysis is done to evaluate the model performance. Then the validation of the soft computational model is made by drawing a comparison with experimental results and other external validation criteria. Moreover, the results and predictions of the presented soft computing model are verified by incorporating a parametric analysis, and the reliability of the model is compared with available models in the literature by an experimental versus theoretical comparison. Based on the findings, the valuation and performance of the proposed model is assessed with other strength models provided in the literature using the collated database. Thus the proposed model outperformed other existing models in term of accuracy and predictability. Both parametric and statistical analysis demonstrate that the proposed model is well trained to efficiently forecast strength of CFRP wrapped structural members. The presented study will promote its utilization in rehabilitation and retrofitting and contribute towards sustainable construction material.
Keywords: carbon fiber-reinforced polymer; multi expression programming; multiphysics model; parametric analysis; prediction.