Topic Editors

Department of Decision and Information Sciences, School of Business Administration, Oakland University, Rochester, MI 48309, USA
Shanghai Engineering Research Center of Urban Infrastructure Renewal, Shanghai 200032, China

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

Abstract submission deadline
10 September 2025
Manuscript submission deadline
10 December 2025
Viewed by
12775

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

  • digital technologies
  • intelligent technologies
  • urban construction
  • urban operation
  • urban maintenance
  • urban renewal
  • building information modeling (BIM)
  • Internet of Things (IoT)
  • artificial intelligence (AI)
  • machine learning
  • smart design
  • real-time monitoring
  • intelligence control
  • predictive maintenance
  • resource management
  • urban planning
  • infrastructure development
  • virtual reality
  • augmented reality
  • blockchain
  • smart grid systems
  • public service delivery
  • facility management
  • digital platforms
  • resilient cities
  • sustainable cities

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
ISPRS International Journal of Geo-Information
ijgi
2.8 6.9 2012 35.8 Days CHF 1900 Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

  1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
  2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
  3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
  4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
  5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (11 papers)

Order results
Result details
Journals
Select all
Export citation of selected articles as:
27 pages, 11777 KiB  
Article
An Adaptive Pedestrian Flow Prediction Model Based on First-Order Differential Error Adjustment and Hidden Markov Model
by Hengyun Zhang, Jianyi Deng, Yiwen Xu, Yichuan Deng and Jia-Rui Lin
Buildings 2025, 15(6), 902; https://doi.org/10.3390/buildings15060902 - 13 Mar 2025
Viewed by 382
Abstract
Pedestrian flow prediction is a quintessential time series forecasting problem with widespread applications in domains such as indoor navigation and emergency response. However, existing prediction models exhibit sensitivity to anomalous data and face significant challenges in adapting to dynamic and evolving environments. To [...] Read more.
Pedestrian flow prediction is a quintessential time series forecasting problem with widespread applications in domains such as indoor navigation and emergency response. However, existing prediction models exhibit sensitivity to anomalous data and face significant challenges in adapting to dynamic and evolving environments. To address these challenges, this paper proposes an integrated pedestrian flow prediction framework. The core architecture employs a Long Short-Term Memory (LSTM) network to capture complex temporal dependencies in pedestrian movement patterns. To improve prediction accuracy, we introduce a two-stage error compensation mechanism. A first-order differential (FoD) module continuously adjusts prediction deviations by analyzing real-time error gradients, while a Hidden Markov Model (HMM)-based adaptive controller dynamically optimizes model parameters in response to changing crowd dynamics. This model effectively mitigates data inconsistency and the challenges associated with a high proportion of zero values. It is designed to provide adaptive feedback and adjust predictions in response to real-time variations in pedestrian flow. For the adjusted time node prediction sequence, accuracy improved by 2.45%, while F-crowded made a significant breakthrough, increasing from 0 to 60.29%. For the full prediction sequence, accuracy increased by 1.9%, from 76.14% to 77.87%, and F-crowded increased by 1.7%, from 85.18% to 86.62%. These results highlight the effectiveness of the HMM-FoD-LSTM model in dealing with data variability in dynamic environments. Full article
Show Figures

Figure 1

21 pages, 2166 KiB  
Article
Optimizing Residential Buildings Desing Using Integrated Project Delivery (IPD) and Building Information Modeling (BIM): A Case Study in Peru
by Delta Salome Tizon Checca, Ecler Mamani Chambi and Alain Jorge Espinoza Vigil
Buildings 2025, 15(6), 901; https://doi.org/10.3390/buildings15060901 - 13 Mar 2025
Viewed by 662
Abstract
Construction projects often exceed budgets and deadlines, evidencing the need for collaborative methodologies such as Integrated Project Delivery (IPD) and Building Information Modeling (BIM). This research evaluates their influence on the design stage of residential buildings through a case study in Peru, managed [...] Read more.
Construction projects often exceed budgets and deadlines, evidencing the need for collaborative methodologies such as Integrated Project Delivery (IPD) and Building Information Modeling (BIM). This research evaluates their influence on the design stage of residential buildings through a case study in Peru, managed by an SME. The methodology includes: (1) diagnosis of management through documentary review and interviews, (2) proposal of tools based on BIM and IPD, and (3) validation through statistical analysis and a validation matrix. Nine typical problems were identified, such as deficiencies in plans, measurements and budgets, and poor planning. Eight optimization tools were proposed, including NEC4 ECC contracts, Trimble Connect, Revit, Navis-works, contractor integration, ICE Sessions, 3D, 4D, and 5D BIM models. The 3D model showed 0.48 interferences per m2, the 4D facilitated the monitoring of progress, and the 5D optimized costs by 5.28%. The validation process highlighted the NEC4 ECC Contract, the integration of the contractor, and the 3D and 5D BIM models (Revit and Navisworks) as the most effective tools. This study provides evidence on the implementation of BIM and IPD to optimize the management of residential buildings. Full article
Show Figures

Figure 1

20 pages, 1803 KiB  
Article
MVSAPNet: A Multivariate Data-Driven Method for Detecting Disc Cutter Wear States in Composite Strata Shield Tunneling
by Yewei Xiong, Xinwen Gao and Dahua Ye
Sensors 2025, 25(6), 1650; https://doi.org/10.3390/s25061650 - 7 Mar 2025
Viewed by 481
Abstract
Disc cutters are essential for shield tunnel construction, and monitoring their wear is vital for safety and efficiency. Due to their position in the soil silo, it is more challenging to observe the wear of disc cutters directly, making accurate and efficient detection [...] Read more.
Disc cutters are essential for shield tunnel construction, and monitoring their wear is vital for safety and efficiency. Due to their position in the soil silo, it is more challenging to observe the wear of disc cutters directly, making accurate and efficient detection a technical challenge. However, existing methods that treat the problem as a classification task often overlook the issue of data imbalance. To solve these problems, this paper proposes an end-to-end detection method for disc cutter wear state called the Multivariate Selective Attention Prototype Network (MVSAPNet). The method introduces an attention prototype network for variable selection, which selects important features from many input parameters using a specialized variable selection network. To address the problem of imbalance in the wear data, a prototype network is used to learn the centers of the normal and wear state classes, and the detection of the wear state is achieved by detecting high-dimensional features and comparing their distances to the class centers. The method performs better on the data collected from the Ma Wan Cross-Sea Tunnel project in Shenzhen, China, with an accuracy of 0.9187 and an F1 score of 0.8978, yielding higher values than the experimental results of other classification models. Full article
Show Figures

Figure 1

27 pages, 4838 KiB  
Article
Analysis of Development Trends and Associations in Intelligent Construction of Chinese Corporations
by Yuhao Wang, Xuefeng Zhao, Xueyao Yu, Siyu Liu, Miao Feng, Yibing Tao and Qiantai Yang
Buildings 2025, 15(5), 716; https://doi.org/10.3390/buildings15050716 - 24 Feb 2025
Viewed by 590
Abstract
Intelligent construction, as a crucial driving force for the transformation and upgrading of the construction industry, is currently reshaping the production processes and management models throughout the entire life cycle of buildings. Nevertheless, construction enterprises are confronted with issues, such as great difficulties [...] Read more.
Intelligent construction, as a crucial driving force for the transformation and upgrading of the construction industry, is currently reshaping the production processes and management models throughout the entire life cycle of buildings. Nevertheless, construction enterprises are confronted with issues, such as great difficulties in system integration, complexity in multi-field collaboration, mismatch of technological requirements, and disharmony between standards and management processes during the process of promoting intelligent construction, which have restricted its in-depth application. This paper adopts a combination of questionnaire surveys and text mining methods to accurately gain insights into the actual situation of the application of intelligent construction in Chinese corporations. Cite Space is utilized to conduct keyword co-occurrence and clustering analyses and to construct the correlation atlas of the intelligent construction system, which are used to conduct in-depth analyses of its development trends and internal correlations. The research results demonstrate that aspects, such as building information modeling (BIM), smart construction sites, intelligent equipment, and prefabricated construction, exhibit significant development trends in the field of intelligent construction. Moreover, the precise matching between technology and the business needs of enterprises is of vital importance for the efficient implementation of intelligent construction. This research provides clear technological and management paths for intelligent construction in Chinese corporations, aiming to promote the standardization process of intelligent construction for enterprises and the industry and to facilitate the digital transformation and upgrading of the construction industry. Full article
Show Figures

Figure 1

24 pages, 8794 KiB  
Article
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
Cited by 1 | Viewed by 919
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
Show Figures

Figure 1

27 pages, 3088 KiB  
Article
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
Cited by 1 | Viewed by 823
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
Show Figures

Figure 1

21 pages, 6950 KiB  
Article
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 1081
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
Show Figures

Figure 1

36 pages, 11665 KiB  
Article
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
Cited by 1 | Viewed by 2011
Abstract
This paper presents COWINE (Community Twin Ecosystem), an ecosystem that harnesses Digital Twin (DT) to elevate and transform community resilience strategies. COWINE 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 COWINE (Community Twin Ecosystem), an ecosystem that harnesses Digital Twin (DT) to elevate and transform community resilience strategies. COWINE 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. COWINE 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 COWINE’s collaborative use with the involved parties in managing tornado scenarios. This study demonstrates how COWINE 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 COWINE. The findings show COWINE’s potential to be utilized as a collaborative tool for community disaster resilience management. Full article
Show Figures

Figure 1

16 pages, 5831 KiB  
Article
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 1020
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
Show Figures

Figure 1

31 pages, 17520 KiB  
Article
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
Cited by 1 | Viewed by 1167
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
Show Figures

Figure 1

23 pages, 1437 KiB  
Article
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 1998
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
Show Figures

Figure 1

Back to TopTop