Abstract
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.
Highlights
The results reveal that the suggested machine learning models apply to a wide range of input data, boosting their utility
The mathematical equations for the computation of carbon fiber-reinforced polymer (CFRP) confined concrete strength consist of five input parameters derived by decoding the developed model generated by Multi Expression Programming” (MEP)
828 data points have been added into the assembled database for forecasting f 0 cc, which is another fascinating part of this work; as a result, high precision with few discrepancies has been obtained
Summary
Compared to steel and other retrofitting techniques, carbon fiber-reinforced polymer (CFRP) possesses significant properties such as high tensile strength, resistance towards the corrosive environment, minimal maintenance, improved aesthetics, reduced thermal and electrical conductivity, strong resistance to chemical assaults, stress durability, and geometric compatibility. When earthquakes inflict damage to concrete structures, their strength and serviceability deteriorate, and their restoration requires retrofitting or rehabilitation through different techniques. In this regard, jacketing through CFRP is considered dominant in contrast to steel and concrete jacketing due to many aspects, including convenient handling and installation, minimal disruption of structure, and reduced time utilization. CFRPs being popular in other domains can be incorporated for retrofitting and rehabilitation of buildings and bridges to enhance the strength and efficacy of structures [1,2,3,4,5,6,7]
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