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	17.8 Days	CHF 2400	Submit
Buildings buildings	3.1	3.4	2011	17.2 Days	CHF 2600	Submit
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600	Submit
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600	Submit
Smart Cities smartcities	7.0	11.2	2018	25.8 Days	CHF 2000	Submit

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Published Papers (4 papers)

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All Applied Sciences Buildings Energies Sensors Smart Cities

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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 515

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

Viewed by 569

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 623

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 1313

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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