A constrained neural network model for soil liquefaction assessment with global applicability
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A constrained neural network model for soil liquefaction assessment with global applicability Yifan ZHANG, Rui WANG* , Jian-Min ZHANG, Jianhong ZHANG Department of Hydraulic Engineering, State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China *
Corresponding author. E-mail: [email protected]
© Higher Education Press 2020
ABSTRACT A constrained back propagation neural network (C-BPNN) model for standard penetration test based soil liquefaction assessment with global applicability is developed, incorporating existing knowledge for liquefaction triggering mechanism and empirical relationships. For its development and validation, a comprehensive liquefaction data set is compiled, covering more than 600 liquefaction sites from 36 earthquakes in 10 countries over 50 years with 13 complete information entries. The C-BPNN model design procedure for liquefaction assessment is established by considering appropriate constraints, input data selection, and computation and calibration procedures. Existing empirical relationships for overburden correction and fines content adjustment are shown to be able to improve the prediction success rate of the neural network model, and are thus adopted as constraints for the C-BPNN model. The effectiveness of the C-BPNN method is validated using the liquefaction data set and compared with that of several liquefaction assessment methods currently adopted in engineering practice. The C-BPNN liquefaction model is shown to have improved prediction accuracy and high global adaptability. KEYWORDS
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soil liquefaction assessment, case history dataset, constrained neural network model, existing knowledge
Introduction
Consequences of soil liquefaction, such as sand boiling and ejecta, loss of soil strength, lateral spreading, and ground settlement and upheaval, are a major source of seismic hazard [1–4]. Accurate assessment and prediction of liquefaction is an important issue in geotechnical engineering, especially in seismically active areas. Standard penetration tests (SPT) have long been used to develop various prediction methods based on liquefaction case histories. Although recent developments in cone penetration test (CPT) technology has promoted its use in liquefaction assessment [5,6], SPT is still widely used in many parts of the world due to its simplicity, cost efficiency, and accumulation of historic data [7,8], especially in China and Japan. SPT based liquefaction assessment methods can generally be divided into two categories: traditional simplified semi-empirical methods, Article history: Received Nov 7, 2019; Accepted Mar 14, 2020
including the liquefaction cyclic resistance method [9,10], the liquefaction safety factor method [11–13], and the critical SPT-N method [14,15]; data-driven statistical and machine learning methods, such as artificial neural network methods [16,17], decision tree methods [18], support vector machine methods [19,20], and logistic regression methods [21–23]. Data-driven meth
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