پیش بینی مقاومت برشی پانچ در دال های دوطرفه با استفاده از الگوریتم برنامه نویسی ژنتیک و برنامه نویسی جغرافیای زیستی

دال های دو طرفه بتن آرمه یکی از سیستم های مرسوم سازه ای می باشند. مزایای این دال ها باعث کاربرد زیاد آنها در صنعت ساختمان شده است. ولی این سیستم ها با مشکلاتی نظیر برش پانچ مواجه هستند. روابط موجود برای پیش بینی برش پانچ بر اساس نتایج آماری آزمایش های موجود در تحقیقات گذشته بدست آمده اند. با این حال این روابط تقریبی بوده و دارای خطای بالا می باشند. هدف اصلی این مقاله معرفی روشی قابل اعتماد و کاربردی برای محاسبه برش پانچ برای دال های نازک و ضخیم با استفاده از هوش مصنوعی است. برای این کار از برنامه نویسی ژنتیک و برنامه ریزی جغرافیای زیستی برای پیدار کردن رابطه بین ظرفیت برش پانچ و پارامترهای موثر بر آن استفاده شده است. ابتدا 267 داده آزمایشگاهی موجود جمع آوری شده است. سپس با استفاده از روشهای مذکور رابطه ای برای پیش بینی مقاومت برش پانچ ارائه شده است. نتایج نشان می دهد که روشهای مبتنی بر هوش مصنوعی قادرند با خطای متوسط کمتر از 2% در مقابل خطای 14 الی 28 درصدی روابط سنتی آیین نامه ها مقاومت برش پانچ را پیش بینی کند.

prediction of pushing shear capacity in two-way slabs using genetic programming and biogeography-based programming

two-way slabs are one of the common structural systems. the benefits of such systems have led to extensive use of them in building construction. however, these systems are prone to pushing shear problem which causes sudden failure. there are lots of equations to predict punching shear of slabs. the main proportion of the existing equations are based on statistical results from previous experimental studies. however, these equations are approximate and have large errors. therefore, more exact and reliable equations that can estimate punching shear capacity are desirable. the aim of this study is to propose an applicable method to predict punching shear in thin and thick slabs using artificial intelligence. for this reason genetic programming (gp) and biogeography-based programming (bbp) are employed to find a relationship between punching shear and the corresponding effective parameters. gp that is inspired by natural genetic process, searches for an optimum population among the various probable ones. two main operations of gp are crossover and mutation which make it possible to form new generations with better finesses. unlike the gp, bbp is a biogeography-based optimization (bbo) technique which is inspired by the geographical distribution in an ecosystem. bbp employs principles of biogeography to create computer programs. first, 267 experimental data is collected from the past studies. next, using the aforementioned algorithms, a relationship to predict punching shear is proposed. to evaluate the error of prediction, several error functions including rmse, mae, mape, r, and obj are utilized. matlab software is used to build the models of prediction. 10 different models are built and the one with the minimum error is selected. based on the results, gp3 and bbp9 models could reach the best fitness. these models contain 3 sub-trees that use operators of plus, minus, multiplication, division, ln, sin, power 2, power 5 power 0.5, power 0.33, power 0.2, and power 0.25. overall, the final tree includes several variables and integers, the variables are inputs of column dimension, effective depth, rebar ratio, compressive strength of concrete, and yielding strength of the rebars, and the output of punching shear capacity. the results of modeling are compared with recommended values of the aci318 and ec2 codes. comparison shows that code equations are scattered and therefore are not very reliable. maximum error for both model and code equations occurs when the yielding strength of the rebars is low. minimum estimation is related to gp and aci codes with the ratio of 0.485 and 0.52, respectively which is due to very low thickness of the slab (41 to 55 mm). the maximum estimated shear belongs to aci code in which the estimated value is two times the real one. also, standard deviation of aci values is about two times the others. among the code equations, ec2 values yield more accurate results. however, gp and bbp models give much less mean error. also, standard deviation of these methods is less than code values. in total, results show that the methods based on artificial intelligence are able to estimate pushing shear with around 2% error, compared to existing code equations which give 14-28% error.