The adoption of deep neural network (DNN) to the prediction of soil liquefaction based on shear wave velocity

Zhang, Yonggang; Xie, Yuanlun; Zhang, Yan; Qiu, Junbo; Wu, Sunxin

doi:10.1007/s10064-021-02250-1

Cited by 75 publications

(14 citation statements)

References 27 publications

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“…[31] The constraining α th is the linear thermal expansion coefficient of the concrete, the pile concrete strength grade is C80, α th = 1 × 10 −5 / • C, and the elastic modulus is E = 38 Gpa, assuming the elastic modulus and linear thermal expansion coefficient of the concrete to be constant for the temperatures in the paper. The sign of the measured strain was defined to be positive, which is consistent with geotechnical sign conventions [32][33][34][35][36][37][38][39][40][41].…”

Section: Thermo-mechanical Stress Profiles Along With Test Pilementioning

confidence: 87%

Experimental Study of Thermal Response of Vertically Loaded Energy Pipe Pile

Wang

2021

Sustainability

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Energy piles are a relatively new technology that have dual function as heat transferring and load bearing. Due to the influence of temperature cycles, additional thermal stress and relative displacement of the pile will be generated; this is different from the load transferring mechanism of the conventional pile. In order to study the thermodynamic characteristics of the energy pipe pile under dual working conditions and temperature cycles, field tests were carried out on the PHC (prestressed high-strength concrete) energy pipe pile without constraining on the top of the piles. Displacement gauges were arranged on the top of the pile, and concrete strain gauges (temperature, strain) were embedded in the pile. The variation laws of temperature, thermal strain, thermal stress, side friction resistance, and displacement of the pile top during the temperature cycling were analyzed. The test results show that the heat exchange system reached a stable state after being heated for 5 days in summer. The average temperature of the pile increased by 15.17 °C, to 34.68 °C; it was low at both ends and high in the middle part. After 5 days in the winter environment, the average temperature of the pile decreased by 10.09 °C, to 9.54 °C, which was high at both ends and low in the middle. The thermal stress was generated inside the pile, and the maximum compressive stress was 3.446 MPa and the maximum tensile stress was 2.69 MPa. The neutral point of the side friction resistance appeared 8 m below the pile top, about 2/3 of the pile length. The maximum negative side friction resistance under the summer condition was 42.06 KPa, the maximum positive side friction resistance under the winter condition was 29.93 KPa, and the lateral resistance of the pile degraded in winter. Under the influence of thermal load, the final pile top displacements in the summer and winter were −0.7 mm (0.175%D) and 0.77 mm (0.193%D), respectively.

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Section: Thermo-mechanical Stress Profiles Along With Test Pilementioning

confidence: 87%

Experimental Study of Thermal Response of Vertically Loaded Energy Pipe Pile

Wang

2021

Sustainability

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“…But limited to SPT and CPT based, the relevant parameters are not sufficient to improve the performance of these models, shear wave velocity is a more important parameter to reduce field conditions and laboratory environment. For example, Zhang et al [135] proposed a multilayer fully connected network (ML-FCN)) based on the shear wave velocity V s and trained DNN models based on the dataset collected by Hanna et al [136]. The DNN models were trained by V s and SPT data in the dataset and demonstrated that high prediction accuracy could be achieved even without the V s prediction model, while the presence of V s allowed the training rate to be unaffected.…”

Section: Prediction Of Seismic Liquefactionmentioning

confidence: 99%

Recent Advances of Deep Learning in Geological Hazard Forecasting

Wang¹,

Sun²,

Chen³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Geological hazard is an adverse geological condition that can cause loss of life and property. Accurate prediction and analysis of geological hazards is an important and challenging task. In the past decade, there has been a great expansion of geohazard detection data and advancement in data-driven simulation techniques. In particular, great efforts have been made in applying deep learning to predict geohazards. To understand the recent progress in this field, this paper provides an overview of the commonly used data sources and deep neural networks in the prediction of a variety of geological hazards. KEYWORDSGeological hazard; deep learning; neural networks; geohazard data sources; earthquake; volcanic 1 Introduction Geological hazards are caused by endogenous and exogenous geological processes or anthropogenic factors on earth [1,2], which encompass a broad range of abnormal stratigraphic activity or extreme changes in geological environments, such as landslides, avalanches, debris flows, earthquakes, etc. Geological hazards pose a serious threat to human life and property [3]. Only by accurately forecasting the occurrence of geohazards can emergency measures be devised to mitigate the damage caused by the impending disaster. Geological hazard forecasting refers to the use of logical reasoning, numerical simulation, and comprehensive analysis based on historical geological hazard activity patterns, formation conditions, occurrence mechanisms, and other factors to speculate and assess the development and changes of geological hazards and the possible degree of danger and damage in a certain period in the future. Geohazard forecasting has attracted tremendous attention nowadays, particularly considering natural resource scarcity, environmental degradation, population expansion, sustainable development, and world economic integration. Many efforts have been made in forecasting geological hazards in the past decades. The "3S technology", including Global Position System (GPS), Geographic Information System (GIS) [4], and Remote Sensing (RS), has been widely applied in the field of geological hazard forecasting [5]. The

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“…Artificial neural network (ANN), extreme learning machine (ELM), support vector machine (SVM) and other algorithms based mainly on regression / correlation relationships have been successfully applied to progressively improve the prediction performance of variables relevant to the petroleum industry (Farsi et al 2021b). Some of the researchers work on the Vs based on the ML work (Weijun et al 2017;Azadpour et al 2020;Zhang et al 2020;Zhang et al 2021;Olayiwola et al 2021;Zhong et al 2021;Ebrahimi et al 2022).…”

Section: Machine-learning (Ml) Algorithmsmentioning

confidence: 99%

Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms

Rajabi

Hazbeh

Davoodi

et al. 2022

J Petrol Explor Prod Technol

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Shear wave velocity (VS) data from sedimentary rock sequences is a prerequisite for implementing most mathematical models of petroleum engineering geomechanics. Extracting such data by analyzing finite reservoir rock cores is very costly and limited. The high cost of sonic dipole advanced wellbore logging service and its implementation in a few wells of a field has placed many limitations on geomechanical modeling. On the other hand, shear wave velocity VS tends to be nonlinearly related to many of its influencing variables, making empirical correlations unreliable for its prediction. Hybrid machine learning (HML) algorithms are well suited to improving predictions of such variables. Recent advances in deep learning (DL) algorithms suggest that they too should be useful for predicting VS for large gas and oil field datasets but this has yet to be verified. In this study, 6622 data records from two wells in the giant Iranian Marun oil field (MN#163 and MN#225) are used to train HML and DL algorithms. 2072 independent data records from another well (MN#179) are used to verify the VS prediction performance based on eight well-log-derived influencing variables. Input variables are standard full-set recorded parameters in conventional oil and gas well logging data available in most older wells. DL predicts VS for the supervised validation subset with a root mean squared error (RMSE) of 0.055 km/s and coefficient of determination (R2) of 0.9729. It achieves similar prediction accuracy when applied to an unseen dataset. By comparing the VS prediction performance results, it is apparent that the DL convolutional neural network model slightly outperforms the HML algorithms tested. Both DL and HLM models substantially outperform five commonly used empirical relationships for calculating VS from Vp relationships when applied to the Marun Field dataset. Concerns regarding the model's integrity and reproducibility were also addressed by evaluating it on data from another well in the field. The findings of this study can lead to the development of knowledge of production patterns and sustainability of oil reservoirs and the prevention of enormous damage related to geomechanics through a better understanding of wellbore instability and casing collapse problems. Graphical abstract

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The adoption of deep neural network (DNN) to the prediction of soil liquefaction based on shear wave velocity

Cited by 75 publications

References 27 publications

Experimental Study of Thermal Response of Vertically Loaded Energy Pipe Pile

Experimental Study of Thermal Response of Vertically Loaded Energy Pipe Pile

Recent Advances of Deep Learning in Geological Hazard Forecasting

Predicting shear wave velocity from conventional well logs with deep and hybrid machine learning algorithms

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