Show simple item record

contributor authorDe-Cheng Feng
contributor authorWen-Jie Wang
contributor authorSujith Mangalathu
contributor authorErtugrul Taciroglu
date accessioned2022-02-01T22:10:14Z
date available2022-02-01T22:10:14Z
date issued11/1/2021
identifier other%28ASCE%29ST.1943-541X.0003115.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4272758
description abstractRC shear walls are commonly used as lateral load-resisting elements in seismic regions, and the estimation of their shear strengths can become simultaneously design-critical and complex when they have so-called squat geometries, i.e., height-to-length ratios less than two. This paper presents a study on the training and interpretation of an advanced machine-learning model that strategically combines two algorithms for the said purpose. To train the model, a comprehensive shear strength database of 434 samples of squat RC walls is utilized. First, the eXtreme Gradient Boosting (XGBoost) algorithm is used to establish a predictive model for estimating the shear strength, wherein 70% and 30% of the data are respectively used for training and validation. This effort resulted in an approximately 97% validation accuracy, which well exceeds current mechanics-based/semiempirical models. Second, the SHapley Additive exPlanations (SHAP) algorithm is used to estimate the relative importance of the factors affecting XGBoost’s shear strength estimates. This step thus enabled physical and quantitative interpretations of the input-output dependencies, which are nominally hidden in conventional machine-learning approaches. Through this setup, several squat wall attributes are identified as being critical in shear strength estimates.
publisherASCE
titleInterpretable XGBoost-SHAP Machine-Learning Model for Shear Strength Prediction of Squat RC Walls
typeJournal Paper
journal volume147
journal issue11
journal titleJournal of Structural Engineering
identifier doi10.1061/(ASCE)ST.1943-541X.0003115
journal fristpage04021173-1
journal lastpage04021173-13
page13
treeJournal of Structural Engineering:;2021:;Volume ( 147 ):;issue: 011
contenttypeFulltext


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record