Digital and Intelligent Technologies and Application in Urban Construction, Operation, Maintenance, and Renewal

Topic Information

Dear Colleagues,

This topic explores the transformative technologies and impacts of digitalization and artificial intelligence throughout the process of the construction, operation, maintenance, and renewal of urban infrastructure, including contributions on the following three core themes:

(1) Theories and methods on how to enhance the application of technologies such as BIM, the IoT, AI, and machine learning to meet the needs of construction, operation, and renewal;

(2) Digital and smart technologies in urban planning, infrastructure construction, operation, and maintenance;

(3) Theory and application exploration of digital and smart technologies to promote urban renewal and green and sustainable development. The goal of this topic is to foster interdisciplinary dialogue that provides actionable insights for shaping technologically advanced, resilient, and sustainable cities of the future.

Original research articles, review articles, case studies, and conceptual articles are welcome, and comparisons between different urban contexts and technology applications are encouraged.

Prof. Dr. Vijayan Sugumaran
Prof. Dr. Min Hu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Buildings buildings	3.1	3.4	2011	15.3 Days	CHF 2600	Submit
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600	Submit
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600	Submit
Smart Cities smartcities	7.0	11.2	2018	28.4 Days	CHF 2000	Submit

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Published Papers (7 papers)

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24 pages, 8794 KiB  

Open AccessArticle

Intelligent Monitoring System for Deep Foundation Pit Based on Digital Twin

by Peng Pan, Shuo-Hui Sun, Jie-Xun Feng, Jiang-Tao Wen, Jia-Rui Lin and Hai-Shen Wang

Buildings 2025, 15(3), 366; https://doi.org/10.3390/buildings15030366 - 24 Jan 2025

Viewed by 396

Abstract

Underground space development has significantly increased the depth, scale, and complexity of foundation pit engineering. However, monitoring systems lack mechanical analysis models and fail to predict and control construction risks. Additionally, the foundation pit model could not be updated based on on-site observed [...] Read more.

Underground space development has significantly increased the depth, scale, and complexity of foundation pit engineering. However, monitoring systems lack mechanical analysis models and fail to predict and control construction risks. Additionally, the foundation pit model could not be updated based on on-site observed data, leading to inaccurate predictions. This study proposes a DT modeling framework for foundation pits, which is used to simulate, predict, and control the risks associated with the entire excavation process. Consequently, based on the DT modeling framework, a DT foundation pit model (DTFPM) was established using modeling and updating algorithms. This study summarizes and identifies the key modeling parameters of foundation pits. A parametric modeling algorithm based on ABAQUS (v2020) was developed to drive the excavation pit modeling process within seconds. Furthermore, an inverse analysis optimization algorithm based on genetic algorithms (GA) and real-time observed deformation was employed to update the elastic modulus of the soil. The algorithm supports parallel computing and can converge within 10 generations. The prediction error of the model after inverse analysis can be reduced to within 10%. Finally, the authors applied DTFPM to establish an intelligent monitoring system. The focus is on real-time and predictive warnings based on the monitoring deformation of the current construction step and the updated model. This study analyzes a Beijing project case to verify the effectiveness of the system, demonstrating the practical application of the proposed method. The results showed that the DTFPM could accurately simulate the deformation behavior of the foundation pit. The system could provide more timely and accurate safety warnings. The proposed method can potentially contribute to the intelligent construction of foundation pits in the future, both theoretically and practically. Full article

(This article belongs to the Topic Digital and Intelligent Technologies and Application in Urban Construction, Operation, Maintenance, and Renewal)

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27 pages, 3088 KiB  

Open AccessArticle

Research on Integrated Control Strategy for Highway Merging Bottlenecks Based on Collaborative Multi-Agent Reinforcement Learning

by Juan Du, Anshuang Yu, Hao Zhou, Qianli Jiang and Xueying Bai

Appl. Sci. 2025, 15(2), 836; https://doi.org/10.3390/app15020836 - 16 Jan 2025

Viewed by 529

Abstract

The merging behavior of vehicles at entry ramps and the speed differences between ramps and mainline traffic cause merging traffic bottlenecks. Current research, primarily focusing on single traffic control strategies, fails to achieve the desired outcomes. To address this issue, this paper explores [...] Read more.

The merging behavior of vehicles at entry ramps and the speed differences between ramps and mainline traffic cause merging traffic bottlenecks. Current research, primarily focusing on single traffic control strategies, fails to achieve the desired outcomes. To address this issue, this paper explores an integrated control strategy combining Variable Speed Limits (VSL) and Lane Change Control (LCC) to optimize traffic efficiency in ramp merging areas. For scenarios involving multiple ramp merges, a multi-agent reinforcement learning approach is introduced to optimize control strategies in these areas. An integrated control system based on the Factored Multi-Agent Centralized Policy Gradients (FACMAC) algorithm is developed. By transforming the control framework into a Decentralized Partially Observable Markov Decision Process (Dec-POMDP), state and action spaces for heterogeneous agents are designed. These agents dynamically adjust control strategies and control area lengths based on real-time traffic conditions, adapting to the changing traffic environment. The proposed Factored Multi-Agent Centralized Policy Gradients for Integrated Traffic Control in Dynamic Areas (FM-ITC-Darea) control strategy is simulated and tested on a multi-ramp scenario built on a multi-lane Cell Transmission Model (CTM) simulation platform. Comparisons are made with no control and Factored Multi-Agent Centralized Policy Gradients for Integrated Traffic Control (FM-ITC) strategies, demonstrating the effectiveness of the proposed integrated control strategy in alleviating highway ramp merging bottlenecks. Full article

(This article belongs to the Topic Digital and Intelligent Technologies and Application in Urban Construction, Operation, Maintenance, and Renewal)

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21 pages, 6950 KiB  

Open AccessArticle

Mechanism-Driven Intelligent Settlement Prediction for Shield Tunneling Through Areas Without Ground Monitoring

by Min Hu, Pengpeng Zhao, Jing Lu and Bingjian Wu

Smart Cities 2025, 8(1), 6; https://doi.org/10.3390/smartcities8010006 - 27 Dec 2024

Viewed by 701

Abstract

Ground settlement is a crucial indicator for assessing the safety of shield tunneling and its impact on the surrounding environment. However, most existing settlement prediction methods are based on historical data, which can only be applied with effective monitoring conditions. To overcome this [...] Read more.

Ground settlement is a crucial indicator for assessing the safety of shield tunneling and its impact on the surrounding environment. However, most existing settlement prediction methods are based on historical data, which can only be applied with effective monitoring conditions. To overcome this limitation, this paper proposes the mechanism-driven intelligent settlement prediction method (MISPM), which considers the mechanisms of settlement and attitude movements during construction to design new features that can indirectly reflect settlement. Simulation experiments were used to compare the impact of different candidate features and algorithms on prediction performance, verifying the validity and accuracy of the model. The efficacy of MISPM in predicting settlement changes in advance was substantiated by practical engineering applications. Results showed that MISPM could accurately predict settlement changes even without ground monitoring, thereby corroborating its reliability and applicability in supporting safe tunneling in complex geological environments. In the construction of urban infrastructure, this method has the potential to enhance the efficiency of tunnel construction and ensure environmental safety, which is of great significance for the development of smart cities. Full article

(This article belongs to the Topic Digital and Intelligent Technologies and Application in Urban Construction, Operation, Maintenance, and Renewal)

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36 pages, 11665 KiB  

Open AccessArticle

Community Twin Ecosystem for Disaster Resilient Communities

by Furkan Luleci, Alican Sevim, Eren Erman Ozguven and F. Necati Catbas

Smart Cities 2024, 7(6), 3511-3546; https://doi.org/10.3390/smartcities7060137 - 20 Nov 2024

Viewed by 1472

Abstract

This paper presents COWIN^E (Community Twin Ecosystem), an ecosystem that harnesses Digital Twin (DT) to elevate and transform community resilience strategies. COWIN^E aims to enhance the disaster resilience of communities by fostering collaborative participation in the use of its DT among the [...] Read more.

This paper presents COWIN^E (Community Twin Ecosystem), an ecosystem that harnesses Digital Twin (DT) to elevate and transform community resilience strategies. COWIN^E aims to enhance the disaster resilience of communities by fostering collaborative participation in the use of its DT among the decision-makers, the general public, and other involved stakeholders. COWIN^E leverages Cities:Skylines as its base simulation engine integrated with real-world data for community DT development. It is capable of capturing the dynamic, intricate, and interconnected structures of communities to provide actionable insights into disaster resilience planning. Through demonstrative, simulation-based case studies on Brevard County, Florida, the paper illustrates COWIN^E’s collaborative use with the involved parties in managing tornado scenarios. This study demonstrates how COWIN^E supports the identification of vulnerable areas, the execution of adaptive strategies, and the efficient allocation of resources before, during, and after a disaster. This paper further explores potential research directions using COWIN^E. The findings show COWIN^E’s potential to be utilized as a collaborative tool for community disaster resilience management. Full article

(This article belongs to the Topic Digital and Intelligent Technologies and Application in Urban Construction, Operation, Maintenance, and Renewal)

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16 pages, 5831 KiB  

Open AccessArticle

Evaluation of Static Displacement Based on Ambient Vibration for Bridge Safety Management

by Sang-Hyuk Oh, Hyun-Joong Kim, Kwan-Soo Park and Jeong-Dae Kim

Sensors 2024, 24(20), 6557; https://doi.org/10.3390/s24206557 - 11 Oct 2024

Cited by 1 | Viewed by 771

Abstract

The evaluation of bridge safety is closely related to structural stiffness, with dynamic characteristics and displacement being key indicators. Displacement is a significant factor as it is a physical phenomenon that bridge users can directly perceive. However, accurately measuring displacement generally necessitates the [...] Read more.

The evaluation of bridge safety is closely related to structural stiffness, with dynamic characteristics and displacement being key indicators. Displacement is a significant factor as it is a physical phenomenon that bridge users can directly perceive. However, accurately measuring displacement generally necessitates the installation of displacement meters within the bridge substructure and conducting load tests that require traffic closure, which can be cumbersome. This paper proposes a novel method that uses wireless accelerometers to measure ambient vibration data from bridges, extracts mode shapes and natural frequencies through the time domain decomposition (TDD) technique, and estimates static displacement under specific loads using the flexibility matrix. A field test on a 442.0 m cable-stayed bridge was conducted to verify the proposed method. The estimated displacement was compared with the actual displacement measured by a laser displacement sensor, resulting in an error rate of 3.58%. Additionally, an analysis of the accuracy of displacement estimation based on the number of measurement points indicated that securing at least seven measurement points keeps the error rate within 5%. This study could be effective for evaluating the safety of bridges in environments where load testing is difficult or for bridges that require periodic dynamic characteristics and displacement analysis due to repetitive vibrations, and it is expected to be applicable to various types of bridge structures. Full article

(This article belongs to the Topic Digital and Intelligent Technologies and Application in Urban Construction, Operation, Maintenance, and Renewal)

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31 pages, 17520 KiB  

Open AccessArticle

Sparse Temporal Data-Driven SSA-CNN-LSTM-Based Fault Prediction of Electromechanical Equipment in Rail Transit Stations

by Jing Xiong, Youchao Sun, Junzhou Sun, Yongbing Wan and Gang Yu

Appl. Sci. 2024, 14(18), 8156; https://doi.org/10.3390/app14188156 - 11 Sep 2024

Viewed by 922

Abstract

Mechanical and electrical equipment is an important component of urban rail transit stations, and the service capacity of stations is affected by its reliability. To solve the problem of predicting faults in station mechanical and electrical equipment with sparse data, this study proposes [...] Read more.

Mechanical and electrical equipment is an important component of urban rail transit stations, and the service capacity of stations is affected by its reliability. To solve the problem of predicting faults in station mechanical and electrical equipment with sparse data, this study proposes a fault prediction framework based on SSA-CNN-LSTM. Firstly, this article proposes a fault enhancement method for station electromechanical equipment based on TimeGAN, which expands and generates data that conform to the temporal characteristics of the original dataset, to solve the problem of sparse data in the original fault dataset. An SSA-CNN-LSTM model is then established to extract effective data features from low-dimensional data with insufficient feature depth through structures such as convolutional layers and pooling layers in a CNN, determine the optimal hyperparameters, automatically optimize the model network size, solve the problem of the difficult determination of the neural network model size, and achieve accurate prediction of the fault rate of station electromechanical equipment. Finally, an engineering verification was conducted on the platform screen door (PSD) systems in stations on Shanghai Metro Lines 1, 5, 9, and 10. The experiments showed that the proposed prediction method improved the RMSE by 0.000699, the MAE by 0.00042, and the R2 index by 0.109779 when predicting the fault rate data of platform screen doors on all of the lines. When predicting the fault rate data of the screen doors on a single line, the performance of the model was better than that of the CNN-LSTM model optimized with the PSO algorithm. Full article

(This article belongs to the Topic Digital and Intelligent Technologies and Application in Urban Construction, Operation, Maintenance, and Renewal)

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23 pages, 1437 KiB  

Open AccessArticle

A Blockchain-Based Supervision Data Security Sharing Framework

by Jiu Yong, Xiaomei Lei, Zixin Huang, Jianwu Dang and Yangping Wang

Appl. Sci. 2024, 14(16), 7034; https://doi.org/10.3390/app14167034 - 10 Aug 2024

Viewed by 1631

Abstract

Ensuring trust, security, and privacy among all participating parties in the process of sharing supervision data is crucial for engineering quality and safety. However, the current centralized architecture platforms that are commonly used for engineering supervision data have problems such as low data [...] Read more.

Ensuring trust, security, and privacy among all participating parties in the process of sharing supervision data is crucial for engineering quality and safety. However, the current centralized architecture platforms that are commonly used for engineering supervision data have problems such as low data sharing and high centralization. A blockchain-based framework for the secure sharing of engineering supervision data is proposed by utilizing the tamper-proof, decentralized, and traceable characteristics of blockchain. The secure storage of supervision data is achieved by combining it with the IPFS (InterPlanetary File System), reducing the storage pressure of on-chain data. Additionally, a fast data retrieval framework is designed based on the storage characteristics of supervision data. Then, CP-ABE (Ciphertext Policy Attribute Based Encryption) is combined with a data storage framework to ensure the privacy, security, and reliability of supervisory data during the sharing process. Finally, smart contracts are designed under the designed framework to ensure the automatic and trustworthy execution of access control processes. The analysis and evaluation results of the security, encryption and decryption, and cost performance of the proposed blockchain framework show that the encryption and decryption time is completed within 0.1 s, the Gas cost is within the normal consumption range, and the time cost of smart contract invocation does not exceed 5 s, demonstrating good availability and reusability of the method proposed in this article. Full article

(This article belongs to the Topic Digital and Intelligent Technologies and Application in Urban Construction, Operation, Maintenance, and Renewal)

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