ارائه مدل‌هاي تلفيقي MARS-PSO و ELM-PSO درتخمين مقاومت فشاري بتن در ستون‌هاي‌ بتني دايره‌يي محصورشده با الياف پليمري FRP

Development of the integrated MARS-PSO and ELM-PSO models for estimating the compressive strength of concrete in circular concrete columns confined with FRP polymer fibers

هدف از پژوهش حاضر، استفاده از مدل‌هاي هوش مصنوعي رگرسيون اسپلاين چندمتغيره‌ي تطبيقي (M‌A‌R‌S) و ماشين يادگيري سريع (E‌L‌M) جهت تخمين مقاومت فشاري بتن در ستون‌هاي بتني دايره‌يي محصور شده با F‌R‌P است. به علاوه، براي بهبود دقت مدل‌هاي ذكر شده، از الگوريتم ازدحام ذرات (P‌S‌O) نيز در تلفيق با آنها استفاده و دقت مدل‌ها در جهت تخمين مقاومت بررسي و ارزيابي شده است. نتايج نشان مي‌دهند كه به طور كلي مدل‌هاي هوش مصنوعي استفاده شده، مقاومت فشاري ستون‌هاي محصور شده با F‌R‌P را نسبت به مدل‌هاي تحليلي موجود با دقت بيشتري تخمين مي‌زنند. به ويژه مدل تلفيقي M‌A‌R‌S-P‌S‌O، عملكرد بهتري در مقايسه با ديگر مدل‌هاي استفاده شده داشته است، به نحوي كه ضريب‌هاي همبستگي آن در مرحله‌ي آموزش، 0.9972 و در مرحله‌ي آزمايش، 0.9961 بوده است. همچنين تلفيق الگوريتم P‌S‌O با دو مدل M‌A‌R‌S و E‌L‌M، باعث بهبود دقت آنها به ترتيب به اندازه‌ي 6.13 و 4.68 درصد شده است.

Many columns that have been built in the past need to be repaired and strengthened for various reasons such as errors during construction, design errors, changes in the use of the building, changes in regulations, strong beam-weak column conditions and also injuries from accidents. Today, various methods for strengthening and improving structures, especially concrete columns, have become popular. One of the most common methods for reinforcing columns is to confine them using polymer fiber composites. The advantages of this method include increasing the axial and lateral bearing capacity of the column, increasing the compressive and tensile strength of the concrete member and increasing the ductility of the member. The mechanical properties of the concrete confined with FRP polymer fibers may be required for the purpose of design and rehabilitation. Artificial intelligence methods are among the modeling methods that have shown great power to coordinate with engineering problems. The aim of this study is to use multivariate adaptive regression spline (MARS) and extreme learning machine (ELM) artificial intelligence models to estimate the compressive strength of concrete in circular concrete columns confined with FRP polymer fibers. In addition, in order to improve the accuracy of these models, the particle swarm Optimization algorithm (PSO) is used in combination with these models and the accuracy of the models is evaluated to estimate the resistance. The results show that in overall, the used artificial intelligence models estimate the compressive strength of FRP- confined columns more accurately than the existing analytical models. In particular, the integrated MARS-PSO model has better performance compared to other models used, so that this model has a correlation coefficient of 0.9972 in the training stage and 0.9961 in the experimental stage. Also, the combination of PSO algorithm with MARS and ELM models improves their accuracy by 6.13 and 4.68 percent, respectively.