Machine learning-based prediction of soil compression modulus with application of 1D settlement

Zhang, Dongming; Zhang, Jinzhang; Huang, Hongwei; Qi, Chongchong; Chang, Chi Cheng

doi:10.1631/jzus.a1900515

Cited by 41 publications

(11 citation statements)

References 37 publications

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“…Most applications use large-sized, hard-cutting metal materials with excellent mechanical properties, and their production and manufacturing are increasingly dependent on the abilities of heavy-duty manufacturing equipments with the capability of dealing with large-scale applications [1,2]. Heavy manufacturing equipment is a basic and important key component in the production chain of the manufacturing industry, which reflect the extreme manufacturing capacity and manufacturing level of the country [3][4][5]. It is an important guarantee for national defense security and national economic industrial security.…”

Section: Sawing Equipmentsmentioning

confidence: 99%

Key Technologies and Development Trends in Advanced Intelligent Sawing Equipments

Wang

Zhang

Tan

2021

Chin. J. Mech. Eng.

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As a starting point in equipment manufacturing, sawing plays an important role in industrial production. Intelligent manufacturing equipment is an important carrier of intelligent manufacturing technologies. Due to the backwardness of intelligent technology, the comprehensive performance of sawing equipments in China is obviously different from that in foreign countries. State of the art of advanced sawing equipments is investigated along with the technical bottleneck of sawing machine tool manufacturing, and a new industrial scheme of replacing turning-milling by sawing is described. The key technologies of processing-measuring integrated control, multi-body dynamic optimization, the collaborative sawing network framework, the distributed cloud sawing platform, and the self-adapting service method are analyzed; with consideration of the problems of poor processing control stableness, low single machine intelligence level, no on-line processing data service and active flutter suppression of sawing with wide-width and heavy-load working conditions. Suggested directions for further research, industry implementation, and industry-research collaboration are provided.

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Section: Sawing Equipmentsmentioning

confidence: 99%

Key Technologies and Development Trends in Advanced Intelligent Sawing Equipments

Wang

Zhang

Tan

2021

Chin. J. Mech. Eng.

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“…Once the tunnel accident occurs, it will cause enormous casualties, economic losses and social severe adverse effects. Meanwhile, the embedded environment of geo-structures is complex and uncertain [38,46]. The uncertainty can be mainly divided into two categories [10]: spatial variability of soil properties within one nominally homogeneous layer [21,26] and geological uncertainty in heterogeneous layer [8,34].…”

Section: Introductionmentioning

confidence: 99%

Quantitative evaluation of geological uncertainty and its influence on tunnel structural performance using improved coupled Markov chain

et al. 2021

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The geo-structures embedded in the multiple variable strata could be significantly affected by the geological uncertainty. The quantitative evaluation of geological uncertainty and its influence on the structural safety of embedded tunnels are seldom studied in the past. This paper aims to analyse the effect of geological uncertainty on the structural performance of tunnel using the proposed stochastic geological modelling framework. The geological uncertainty is characterized using an improved coupled Markov chain model based on sparse limited boreholes. A mapping approach is presented to solve the mesh asymmetry problem between the simulated strata and the numerical tunnel model. The tunnel structural performance analysis is then conducted based on the combined model considering the geological uncertainty and tunnel structure. A geological uncertainty index (GUI) is proposed to quantitatively evaluate the level of uncertainty of each borehole and the whole site. The effect of the borehole layout scheme on uncertainty evaluation of factor of safety of tunnel structure is investigated by a large number of stratigraphic realizations. Boreholes collected from Norway with relatively more considerable variability and from Shanghai with relatively more minor variability are adopted as case studies to illustrate the proposed probabilistic analysis framework. The results show that the boreholes with larger GUI values and closer to tunnel locations have a greater weight to affect the embedded tunnel structural performance in uncertain geological strata.

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“…Some studies used artificial neural network (ANN) methods in predicting the performance of the RC elements (Erdem, 2010;Amani and Moeini, 2012;Peng et al, 2012). Although ANNs are strong and useful methods for predicting problems, visible models, such as GEP and MLnER, are more useful because these models present a formula for predicting the output; engineers can therefore employ them easily, and a complex device, like a computer, is not needed (Cheng et al, 2020;Shishegaran et al, 2020a;Zhang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Surrogate models for the prediction of damage in reinforced concrete tunnels under internal water pressure

Bigdeli

Shishegaran

Naghsh

et al. 2021

J. Zhejiang Univ. Sci. A

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In the present study, the performance of reinforced concrete tunnel (RCT) under internal water pressure is evaluated by using nonlinear finite element analysis and surrogate models. Several parameters, including the compressive and tensile strength of concrete, the size of the longitudinal reinforcement bar, the transverse bar diameter, and the internal water pressure, are considered as the input variables. Based on the levels of variables, 36 mix designs are selected by the Taguchi method, and 12 mix designs are proposed in this study. Carbon fiber reinforced concrete (CFRC) or glass fiber reinforced concrete (GFRC) is considered for simulating these 12 samples. Principal component regression (PCR), Multi Ln equation regression (MLnER), and gene expression programming (GEP) are employed for predicting the percentage of damaged surfaces (PDS) of the RCT, the effective tensile plastic strain (ETPS), the maximum deflection of the RCT, and the deflection of crown of RCT. The error terms and statistical parameters, including the maximum positive and negative errors, mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of determination, and normalized square error (NMSE), are utilized to evaluate the accuracy of the models. Based on the results, GEP performs better than other models in predicting the outputs. The results show that the internal water pressure and the mechanical properties of concrete have the most effect on the damage and deflection of the RCT.

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Machine learning-based prediction of soil compression modulus with application of 1D settlement

Cited by 41 publications

References 37 publications

Key Technologies and Development Trends in Advanced Intelligent Sawing Equipments

Key Technologies and Development Trends in Advanced Intelligent Sawing Equipments

Quantitative evaluation of geological uncertainty and its influence on tunnel structural performance using improved coupled Markov chain

Surrogate models for the prediction of damage in reinforced concrete tunnels under internal water pressure

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