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Research on Intelligent Geotechnical Engineering
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Research on Intelligent Geotechnical Engineering

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 7567

Special Issue Editors

Dr. Qinglong Zhang

E-Mail Website
Guest Editor
School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: intelligent geotechnical engineering; intelligent construction and intelligent operation and maintenance; building automation and robot technology
Dr. Guangchang Yang

E-Mail Website
Guest Editor
School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: geotechnical constitutive model; intelligent simulation and modeling; intelligent diagnosis method
Prof. Dr. Yan Yan

E-Mail Website
Guest Editor
School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China
Interests: intelligent geotechnical engineering; intelligent construction and intelligent operation and maintenance
Dr. Hai Shi

E-Mail Website
Guest Editor
School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: intelligent geotechnical engineering; machine learning algorithms for applications in geotechnical engineering; artificial intelligence and disaster prevention
Dr. Yajian Wang

E-Mail Website
Guest Editor
School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China
Interests: intelligent simulation and modeling; application of big data; artificial intelligence; digital twins in underground infrastructures

Special Issue Information

Dear Colleagues,

The prosperous development of infrastructure construction has driven various constructions such as building, municipal administration, energy, water conservancy, shipping, mining, and national defense, in which geotechnical engineering plays an important role. Geotechnical engineering studies geotechnical and soil problems, including issues such as foundations, slopes, and underground engineering. The Fourth Industrial Revolution, centered around technologies such as the Internet of Things, modern communication, big data, and artificial intelligence, has become a platform for intelligent upgrading in many research fields. Under the conditions of this new era, traditional geotechnical engineering research has encountered unprecedented opportunities as well as challenges. The integration of geotechnical engineering with the latest information technology and computer science technology, such as building information models, the Internet of Things, artificial intelligence, deep learning, augmented reality, etc., can help in achieving the intelligent transformation of geotechnical engineering.

This Special Issue aims to highlight the latest innovations in theories, technologies, and methods in intelligent geotechnical engineering which can potentially contribute to the intelligent transformation of geotechnical engineering. We invite submissions of original research articles and reviews. Potential areas may include (but not limited to) the following:

  • Intelligent simulation and modeling;
  • Intelligent monitoring and early warning;
  • Intelligent perception and analysis based on Edge-Cloud-Network;
  • Intelligent decision making and control for construction and operation and maintenance;
  • Integrated parameter intelligent inversion analysis method of geotechnical engineering;
  • Artificial intelligence and disaster prevention and mitigation;
  • Application of big data, artificial intelligence, and digital twins in geotechnical engineering.

We look forward to receiving your contributions.

Dr. Qinglong Zhang
Dr. Guangchang Yang
Prof. Dr. Yan Yan
Dr. Hai Shi
Dr. Yajian Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • 3D geological modeling
  • Internet of Things
  • digital twin
  • big data
  • artificial intelligence
  • edge-cloud-network
  • perception and analysis
  • decision-making
  • active control
  • geotechnical engineering

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

19 pages, 4584 KiB  
Article
Model for Impacts of Urban Water Blue Visual Index and Flow Velocity on Human Brain State and Its Practical Application
by Yiming Zhang, Xuezhou Zhu and Qingbin Li
Buildings 2025, 15(3), 339; https://doi.org/10.3390/buildings15030339 - 23 Jan 2025
Viewed by 771
Abstract
This study develops a predictive model to assess the impacts of urban water blue visual index (BVI) and flow velocity on human brain states using EEG and HRV data in virtual reality simulations. By integrating Gaussian process regression (GPR) and artificial neural networks [...] Read more.
This study develops a predictive model to assess the impacts of urban water blue visual index (BVI) and flow velocity on human brain states using EEG and HRV data in virtual reality simulations. By integrating Gaussian process regression (GPR) and artificial neural networks (ANN), the model accurately captures the relationships between BVI, flow velocities, and brain states, reflecting experimental observations with high precision. Applied across 31 provinces in China, the model effectively predicted regional brain state levels, aligning closely with the birthplace distribution of high-level talents, such as academicians and Changjiang scholars. These results highlight the model’s practical application in optimizing urban water features to enhance mental health, cognitive performance, and societal development. Full article
(This article belongs to the Special Issue Research on Intelligent Geotechnical Engineering)
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21 pages, 7600 KiB  
Article
A Multi-Objective Prediction XGBoost Model for Predicting Ground Settlement, Station Settlement, and Pit Deformation Induced by Ultra-Deep Foundation Construction
by Guangkai Huang, Zhijian Liu, Yajian Wang and Yuyou Yang
Buildings 2024, 14(9), 2996; https://doi.org/10.3390/buildings14092996 - 21 Sep 2024
Cited by 5 | Viewed by 1492
Abstract
Building a deep foundation pit in urban centers frequently confronts issues such as closeness to structures, high excavation depths, and extended exposure durations, making monitoring and prediction of the settlement and deformation of neighboring buildings critical. Machine learning and deep learning models are [...] Read more.
Building a deep foundation pit in urban centers frequently confronts issues such as closeness to structures, high excavation depths, and extended exposure durations, making monitoring and prediction of the settlement and deformation of neighboring buildings critical. Machine learning and deep learning models are more popular than physical models because they can handle dynamic process data. However, these models frequently fail to establish an appropriate balance between accuracy and generalization capacity when dealing with multi-objective prediction. This work proposes a multi-objective prediction model based on the XGBoost algorithm and introduces the Random Forest Bayesian Optimization method for hyperparameter self-optimization and self-adaptation in the prediction process. This model was trained with monitoring data from a deep foundation pit at Luomashi Station of Chengdu Metro Line 18, which are characterized by a sand and pebble stratum, cut-and-cover construction, and a depth of 45.5 m. Input data of the model included excavation rate, excavation depth, construction time, shutdown time, and dewatering; output data included settlement, ground settlement, and pit deformation at an operating metro station only 5.7 m adjacent to the ongoing pits. The training effectiveness of the model was validated through its high R2 scores in both training and test sets, and its generalization ability and transferability were evaluated through the R2 calculated by deploying it on adjacent monitoring data (new data). The multi-objective prediction model proposed in this paper will be promising for monitoring the data processing and prediction of settlement of surrounding buildings for ultra-deep foundation pit engineering. Full article
(This article belongs to the Special Issue Research on Intelligent Geotechnical Engineering)
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13 pages, 6638 KiB  
Article
A Prediction Model for Soil–Water Characteristic Curve Based on Machine Learning Considering Multiple Factors
by Guangchang Yang, Jianping Liu, Yang Liu, Nan Wu and Tingguang Liu
Buildings 2024, 14(7), 2087; https://doi.org/10.3390/buildings14072087 - 8 Jul 2024
Cited by 1 | Viewed by 1589
Abstract
Aiming at the problem of long soil–water characteristic curve (SWCC) testing times and the difficulty of prediction accuracy in complex environments, this paper establishes a SWCC prediction model based on a neural network machine learning algorithm which can take into account the influence [...] Read more.
Aiming at the problem of long soil–water characteristic curve (SWCC) testing times and the difficulty of prediction accuracy in complex environments, this paper establishes a SWCC prediction model based on a neural network machine learning algorithm which can take into account the influence of multiple factors such as temperature, deformation, and salinity. The input layer of the model can reflect the physical properties of the soil and the influence of the external environment, while the suction is taken as an input variable, which in turn can directly obtain the water content under the corresponding conditions. The predictive ability of the model is verified by comparing and analyzing the predicted results of the SWCC under different temperature, void ratio, and salinity conditions with the experimental results. The research in this paper provides a new method for predicting the SWCC considering multiple factors, and the prediction accuracy of the model is related to the amount of experimental data. Full article
(This article belongs to the Special Issue Research on Intelligent Geotechnical Engineering)
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15 pages, 3184 KiB  
Article
Monitoring Data Fusion Model for Subsoil Layer Deformation Prediction
by Huiguo Wu, Yuedong Wu, Jian Liu, Lei Zhang, Yongyang Zhu and Chuanyang Liang
Buildings 2024, 14(7), 2055; https://doi.org/10.3390/buildings14072055 - 5 Jul 2024
Viewed by 921
Abstract
Predicting soil deformation is critical for the success of building construction projects. The traditional methods used for this task, which rely on theoretical calculations and numerical simulations, require detailed information on soil characteristics and geological conditions. These essential details are often challenging to [...] Read more.
Predicting soil deformation is critical for the success of building construction projects. The traditional methods used for this task, which rely on theoretical calculations and numerical simulations, require detailed information on soil characteristics and geological conditions. These essential details are often challenging to obtain in practical engineering, thereby limiting the accuracy of these methods in building construction contexts. Deep learning (DL) provides a direct approach for modeling soil deformation without having a detailed understanding of the soil properties and geological conditions. However, the existing DL algorithms mainly focus on modeling deformation directly. With advancements in monitoring technology, integrating diverse monitoring data has become crucial for accurately predicting deformation, a need often overlooked in current practices. This paper introduces a monitoring data fusion (MDF) model aimed at enhancing the utilization efficiency of diverse monitoring data. Validated against real-world engineering scenarios, this model significantly outperforms traditional single-feature and multi-feature long short-term memory (LSTM) models. It achieves a mean absolute percentage error (MAPE) of approximately 2.12%, representing reductions of 30% and 63%, and a root mean square error (RMSE) of around 12.5 mm, with reductions of 36% and 77%. Additionally, the DL interpretability method, Shapley additive explanations (SHAP), is utilized to elucidate how various model features contribute to generating predictions. Full article
(This article belongs to the Special Issue Research on Intelligent Geotechnical Engineering)
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18 pages, 5355 KiB  
Article
Research on Collapse Risk Assessment of Karst Tunnels Based on BN Self-Learning
by Jinglai Sun, Yan Wang, Xu Wu, Xinling Wang, Hui Fang and Yue Su
Buildings 2024, 14(3), 685; https://doi.org/10.3390/buildings14030685 - 5 Mar 2024
Cited by 1 | Viewed by 1327
Abstract
The high risk of collapse is a key issue affecting the construction safety of karst tunnels. A risk assessment method for karst tunnel collapse based on data-driven Bayesian Network (BN) self-learning is proposed in this study. The finite element calculation is used to [...] Read more.
The high risk of collapse is a key issue affecting the construction safety of karst tunnels. A risk assessment method for karst tunnel collapse based on data-driven Bayesian Network (BN) self-learning is proposed in this study. The finite element calculation is used to analyze the distribution law of the plastic zone of the tunnel and the karst cave surrounding rock under different combinations of parameters, and a four-factor three-level data case database is established. Through the self-learning of the BN database, a Bayesian Network model of karst tunnel collapse risk assessment with nodes of four types of karst cave parameters is established. The specific probability distribution state and sensitivity of the parameters of different types of karst caves under the condition of whether the tunnel and the karst cave plastic zone are connected or not are studied. The research results show that the distance and angle of the karst cave are the main influencing parameters of the tunnel collapse probability, and the diameter and number of the karst cave are the secondary influencing parameters. Among them, the distance, diameter, and number of karst caves are proportional to the probability of tunnel collapse, and the most unfavorable orientation of karst caves is 45° above the tunnel. When the tunnel passes through the karst area, it should avoid the radial intersection with the karst cave at the arch waist while staying away from the karst cave. The results of this work can provide a reference for the construction safety of karst tunnels under similar conditions. Full article
(This article belongs to the Special Issue Research on Intelligent Geotechnical Engineering)
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