Pin Zhang

Pin Zhang
The Hong Kong Polytechnic University | PolyU · Department of Civil and Environmental Engineering

Doctor of Engineering
Visiting scholar at University of Oxford

About

49
Publications
16,312
Reads
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829
Citations
Introduction
Feel free to communicate about research works. My Email is: pin-cee.zhang@connect.polyu.hk

Publications

Publications (49)
Article
There is considerable potential for data‐driven modelling to describe path‐dependent soil response. However, the complexity of soil behaviour imposes significant challenges on the training efficiency and the ability to generalise. This study proposes a novel physics‐constrained hierarchical (PCH) training strategy to deal with existing challenges i...
Article
Full-text available
This study systematically presents the application of machine learning (ML) algorithms for constructing a constitutive model for soils. A genetic algorithm is integrated with ML algorithms to determine the global optimum model, and the k-fold cross-validation method is used to enhance the models’ robustness. Three typical ML algorithms with formula...
Article
This study develops a novel method for reconstructing three dimensional (3D) granular grains from computed tomography (CT) images. Unlike previous studies requiring trial-and-error hyperparameters, the hybrid algorithm introduced here, integrating the random forest (RF) algorithm and enhanced by particle swarm optimization for automatic determinati...
Article
Coupled hydromechanical finite element modelling of granular soils, taking into account internal erosion, is computationally prohibitive. Alternative data-driven approaches require large datasets for training and often provide poor generalization ability. To overcome these issues, this study proposes a ‘physics-informed multi-fidelity residual neur...
Article
To identify all desired shape parameters of granular particles with less computational cost, this study proposes a three-dimensional convolutional neural network (3D-CNN) based model. Datasets are made of 100 ballast and 100 Fujian sand particles, and the shape parameters (i.e., aspect ratio, roundness, sphericity, and convexity) obtained by conven...
Article
Different from conventional methodology, this study presents an intelligent approach to fast identify the grain-size distribution (GSD) of granular soils using a convolutional neural network (CNN) under a deep learning framework. A database including 279 images of granular soils with their GSDs is first created. Then, the framework of the CNN is ta...
Article
This study adopts the Bayesian neural network (BNN) integrated with a strong non-linear fitting capability and uncertainty, which has not previously been used in geotechnical engineering, to propose a modelling strategy in developing prediction models for soil properties. The compression index Cc and undrained shear strength su of clays are selecte...
Article
Machine learning (ML) holds significant potential for predicting soil properties in geotechnical design but at the same time poses challenges, including those of how to easily examine the performance of an algorithm and how to select an optimal algorithm. This study first comprehensively reviewed the application of ML algorithms in modelling soil p...
Article
Deep learning (DL) algorithm bidirectional long short-term memory (BiLSTM) neural network is employed to model behaviors of the soil-structure interface in this study, as a pioneer research work to investigate the feasibility of using DL to model interface behaviors. Datasets are collected from 12 constant normal stress and 20 constant normal stiff...
Article
It will be practically useful to know the mechanical properties of granular materials by only taking a photo of particles. This study attempts to deal with this challenge by developing a novel deep learning-based modelling strategy. In this strategy, the convolutional neural network (CNN) as image identification algorithm is first used to extract t...
Article
Uncertainty is a commonplace and significant issue in geotechnical engineering. Unlike conventional statistical and machine learning methods, this study presents a novel approach to correlating soil properties that takes uncertainty into account using an artificial neural network with Monte Carlo dropout (ANN_MCD). An uncertainty model for two impo...
Article
Shield machine performance and tunnelling-induced settlement are the main concerns during the tunnelling process. This study proposes an artificial intelligence Internet of Things (AIoT)-based system for real-time monitoring of tunnel construction. Shield machine operational parameters and tunnelling-induced settlement can be transferred and stored...
Data
This database was applied in the following research work: A novel deep learning-based modelling strategy from image of particles to mechanical properties for granular materials with CNN and BiLSTM. Computer Methods in Applied Mechanics and Engineering, 382, 113858. https://doi.org/10.1016/j.cma.2021.113858
Code
The GUI has removed to GitHub: https://github.com/PinZhang3/ErosMLM. This is a machine learning based graphical user interface platform for modelling soil properties or similar issues in other domains. This GUI and source code is open to community now. It includes source code, data, tutorial in both Chinese and English, and manual book. Welcome to...
Article
Machine learning (ML) may provide a new methodology to directly learn from raw data to develop constitutive models for soils by using pure mathematic skills. It has presented success and versatility in cases of simple stress paths due to its strong non-linear mapping capacity without limitations of constitutive formulations. However, current studie...
Article
The straightforward prediction for the air-entry value of compacted soils is practically useful, but the investigation on this issue is scarce. This study presents three alternative straightforward prediction models for the air-entry value of compacted soils using the representative machine learning algorithms of multi expression programming (MEP),...
Article
The interaction between a shield machine and the ground is a complicated problem involving numerous extrinsic and intrinsic factors. Machine learning (ML) algorithms have been recently employed to predict tunnel-soil interactions. This study introduces a more powerful algorithm termed the deep learning (DL) long short-term memory (LSTM) neural netw...
Article
Prediction of ground responses is important for improving performance of tunneling. This study proposes a novel reinforcement learning (RL) based optimizer with the integration of deep-Q network (DQN) and particle swarm optimization (PSO). Such optimizer is used to improve the extreme learning machine (ELM) based tunneling-induced settlement predic...
Article
To reduce the computational cost and improve the accuracy in predicting failure envelopes of caisson foundations , this study proposes an intelligent method using random forest (RF) based on data extended from experiments and calibrated numerical simulations. Two databases are built from the numerical results by coupled Lagrangian finite element me...
Article
It is an ongoing problem to develop a solution to predict ground deformation induced by shallow tunnels construction in dry soils. This study proposes a closed-form elastic analytical solution and a plastic analytical solution for calculating the longitudinal settlement trough. Meanwhile , a semi-analytical solution is further developed for better...
Article
Machine learning (ML) algorithms have been gradually used in predicting tunneling-induced settlement, but there is no uniform process for establishing ML models and even obviously exists deficiency in the existing settlement prediction ML models. This study systematically demonstrates the process of application of machine learning (ML) algorithms i...
Article
This study proposes a hybrid surrogate modelling approach with the integration of deep learning algorithm long short-term memory (LSTM) to identify the mechanical responses of caisson foundations in marine soils. The LSTM based surrogate model is first trained based on limited results generated from the SPH-SIMSAND based numerical simulations with...
Article
Modelling cyclic behaviour of granular soils under both drained and undrained conditions with a good performance is still a challenge. This study presents a new way of modelling the cyclic behaviour of granular materials using deep learning. To capture the continuous cyclic behaviour in time dimension, the long short‐term memory (LSTM) neural netwo...
Article
Full-text available
Compression index Cc is an essential parameter in geotechnical design for which the effectiveness of correlation is still a challenge. This paper suggests a novel modelling approach using machine learning (ML) technique. The performance of five commonly used machine learning (ML) algorithms, i.e. back-propagation neural network (BPNN), extreme lear...
Data
Random forest based artificial intelligent model for predicting failure envelopes of caisson foundations in sand, Applied Ocean Research 101, 102223. https://www.sciencedirect.com/science/article/pii/S0886779820305472
Article
This study presents an application of horizontal spoil discharge jet-grouting (SDJG) columns in soil improvement between the new under-crossing tunnels and the existing tunnels in Changsha, China. To investigate the impact of SDJG construction on the ground and existing tunnels, the ground pore-water pressure, induced hoop strain, vertical displace...
Data
Zhang P, Yin ZY, Zheng YY, Gao FP, 2020. A LSTM Surrogate Modelling Approach for Caisson Foundations. Ocean Engineering, 204, 107263. https://www.sciencedirect.com/science/article/pii/S0029801820303115
Article
Tunneling in mixed ground faces great challenges in control of shield machine, and improper operation easily trigger hazards without warning. Recently, an unexpected ground surface settlement of approximately 0.05 m was observed in the mixed ground of Changsha, China. When the shield machine advanced from the low-permeability ground to the high-per...
Code
The code was from the paper: Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms. 2021, Geoscience Frontiers. 12 (1), 441-452 https://doi.org/10.1016/j.enggeo.2019.105328
Data
This database was first used in the paper "A critical evaluation of machine learning and deep learning in shield-ground interaction prediction, Tunnelling and Underground Space Technology 106, 103593, https://doi.org/10.1016/j.tust.2020.103593" https://www.sciencedirect.com/science/article/pii/S0886779820305472
Data
Zhang P, Yin ZY, Jin YF, Ye GL, 2020. An AI-based model for describing cyclic characteristics of granular materials. International Journal for Numerical and Analytical Methods in Geomechanics, 44, 9: 1315-1335 https://onlinelibrary.wiley.com/doi/full/10.1002/nag.3063
Data
Zhang P, Yin ZY, Jin YF, Ye GL, 2020. An AI-based model for describing cyclic characteristics of granular materials. International Journal for Numerical and Analytical Methods in Geomechanics, 44, 9: 1315-1335 https://onlinelibrary.wiley.com/doi/full/10.1002/nag.3063
Article
Feature selection (FS) is vitally important for determining the optimum subsets of features with effective information and maximizing the model accuracy. This study proposes a novel FS method based on global sensitivity analysis (GSA) for effectively determining the most relevant feature subsets and improving prediction performance of machine learn...
Data
Zhang P, Chen RP, Wu HN, 2019. Real-time Analysis and Regulation of EPB Shield Steering Using Random Forest. Automation in Construction. 106: 101860. https://www.sciencedirect.com/science/article/pii/S0926580518311488
Article
Long-term settlement issues in engineering practice are controlled by the creep index, Cα, but current empirical models of Cα are not sufficiently reliable. In a departure from previous correlations, this study proposes a hybrid surrogate intelligent model for predicting Cα. The new combined model integrates a meta-heuristic particle optimization s...
Data
This code and database were from the paper: Zhang P, Yin ZY, Jin YF, Chan THT, 2020. A novel hybrid surrogate intelligent model for creep index prediction based on particle swarm optimization and random forest, Engineering Geology, 265, 105328. https://doi.org/10.1016/j.enggeo.2019.105328 https://www.sciencedirect.com/science/article/pii/S001379521...
Article
Settlement control is an essential part of the tunnel construction process. This paper proposes two novel computational models based on the Random Forest (RF) algorithm for supporting automatically steering Earth Pressure Balanced (EPB) shield. The first model is utilized for predicting tunneling-induced settlement and the other estimates shield op...
Article
Full-text available
In order to determine the appropriate model for predicting the maximum surface settlement caused by EPB shield tunneling, three artificial neural network (ANN) methods, back-propagation (BP) neural network, the radial basis function (RBF) neural network, and the general regression neural network (GRNN), were employed and the results were compared....
Article
Predicting the tunneling-induced maximum ground surface settlement is a complex problem since the settlement depends on plenty of intrinsic and extrinsic factors. This study investigates the efficiency and feasibility of six machine learning (ML) algorithms, namely, back-propagation neural network, wavelet neural network, general regression neural...
Chapter
This paper presents a case of a single tunnel excavated beneath closely spaced existing twin tunnels with reinforced MJS piles in Changsha, China. Due to soil disturbance and stress re-distribution induced by tunneling, the safety of overlying tunnels during excavating becomes a serious issue. To solve this problem, the Metro Jet System (MJS) metho...

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