Predicting torsional capacity of reinforced concrete members by data-driven machine learning models

Due to the complicated three-dimensional behaviors and testing limitations of reinforced concrete(RC)members in torsion,torsional mechanism exploration and torsional performance prediction have always been difficult.In the present paper,several machine learning models were applied to predict the torsional capacity of RC members.Experimental results of a total of 287 torsional specimens were collected through an overall literature review.Algorithms of extreme gradient boosting machine(XGBM),random forest regression,back propagation artificial neural network and support vector machine,were trained and tested by 10-fold cross-validation method.Predictive performances of proposed machine learning models were evaluated and compared,both with each other and with the calculated results of existing design codes,i.e.,GB 50010,ACI 318-19,and Eurocode 2.The results demonstrated that better predictive performance was achieved by machine learning models,whereas GB 50010 slightly overestimated the torsional capacity,and ACI 318-19 and Eurocode 2 underestimated it,especially in the case of ACI 318-19.The XGBM model gave the most favorable predictions with R^(2)=0.999,RMSE=1.386,MAE=0.86,andλ=0.976.Moreover,strength of concrete was the most sensitive input parameters affecting the reliability of the predictive model,followed by transverse-to-longitudinal reinforcement ratio and total reinforcement ratio.

Calculation example of full cycle carbon emission of super deep foundation pit and carbon reduction measures

The determination of carbon emission from foundation pit engineering is a tough and complex project owing to its characteristics including large material consumption,short use time,difficult recycling and no operation stage.To overcome these limitations,the calculation boundary and calculation method for carbon emission of foundation pit project are defined in this paper,which is successfully applied in the carbon emission analysis of the actual engineering project,i.e.the construction of large-scale foundation pit of Kunming comprehensive transportation international hub.All the carbon emissions coresponding to four working stages including building materials production,building materials transportation,construction and demolition were calculated and anatomized.The results revealed that the content of CO_(2) released in the stage of building materials production accounts for 89.3%of the total carbon emission,which means the amount of building materials consumed in the engineering project is the crucial factor to control the carbon emission.Besides,two kinds of carbon reduction measures,i.e.optimization design of support scheme and recycling waste materials of internal support demolition,were explored by analyzing the proportion and average value of carbon emission from different sub project of the support structure.A pronounced effect of carbon reduction was achieved.Furthermore,both a fast calculation method of carbon emission factor of unit work volume and general carbon reduction measures are proposed in this paper,which could provide a reference and new viewpoint for the engineers and designers to calculate and analyze the carbon emission and to take effective carbon reduction measures.