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

Abstract submission deadline

5 September 2026

Manuscript submission deadline

5 November 2026

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53896

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	2.9	6.1	2011	15 Days	CHF 2400	Submit
Buildings buildings	3.4	5.6	2011	14.7 Days	CHF 2600	Submit
Energies energies	3.9	8.3	2008	16.7 Days	CHF 2600	Submit
ISPRS International Journal of Geo-Information ijgi	3.2	6.7	2012	34.9 Days	CHF 1900	Submit
Sensors sensors	4.0	9.4	2001	17.8 Days	CHF 2600	Submit
Smart Cities smartcities	6.6	13.0	2018	25.1 Days	CHF 2000	Submit

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Published Papers (25 papers)

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28 pages, 8709 KB  

Open AccessArticle

Causal–Semantic Spatiotemporal Traffic Flow Forecasting for Expressway UAV Pre-Deployment Using ETC Gantry Networks

by Zeen Yang, Zhuoer Wang, Hongjuan Zhang and Bijun Li

ISPRS Int. J. Geo-Inf. 2026, 15(8), 354; https://doi.org/10.3390/ijgi15080354 - 6 Aug 2026

Abstract

Expressway unmanned aerial vehicle (UAV) pre-deployment is a geospatial decision-support task that requires reliable road-segment-level traffic flow prediction based on spatial sensing networks. However, existing spatiotemporal forecasting models remain limited in characterizing cross-segment propagation relationships, long-lag causal dependencies, and atypical traffic evolution patterns. [...] Read more.

Expressway unmanned aerial vehicle (UAV) pre-deployment is a geospatial decision-support task that requires reliable road-segment-level traffic flow prediction based on spatial sensing networks. However, existing spatiotemporal forecasting models remain limited in characterizing cross-segment propagation relationships, long-lag causal dependencies, and atypical traffic evolution patterns. In addition, complex models often fail to meet the computational requirements of edge-device deployment. Based on electronic toll collection (ETC) gantry data, this study proposes a causal–semantic spatiotemporal forecasting framework for long-term traffic flow prediction with a 24 h forecasting horizon. First, conditional Granger causality analysis is used to construct a directed causal prior graph that characterizes traffic propagation relationships among expressway segments. Second, scenario-semantic priors generated by a large language model are introduced to describe atypical traffic conditions. Then, causal structural priors and scenario-semantic priors are integrated into a teacher model and transferred to a lightweight student model through response-level and feature-level knowledge distillation. Experiments using expressway data from Hubei Province, China, show that the proposed model achieves the best overall performance in the typical scenario and competitive performance in the atypical scenario. The results indicate that the proposed framework can provide day-scale decision support for expressway law-enforcement UAV pre-deployment and enhance the spatial intelligence of traffic emergency 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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26 pages, 15304 KB  

Open AccessArticle

Denoising Method for Pipeline Leakage Voiceprint in Utility Tunnel Using an Enhanced StarGAN

by Qi-Wen Tian, Yu-Fei Chen, Shi-Wan Zhang, Hui-Qing Lan and Jie Gao

Smart Cities 2026, 9(7), 116; https://doi.org/10.3390/smartcities9070116 - 9 Jul 2026

Viewed by 339

Abstract

Leakage of water pipelines in urban underground utility tunnels poses a major threat to tunnel safety and operation; therefore, voiceprint recognition is adopted for real-time pipeline monitoring. Although utility tunnels are less affected by outdoor interference, multiple internal noises still degrade voiceprint recognition [...] Read more.

Leakage of water pipelines in urban underground utility tunnels poses a major threat to tunnel safety and operation; therefore, voiceprint recognition is adopted for real-time pipeline monitoring. Although utility tunnels are less affected by outdoor interference, multiple internal noises still degrade voiceprint recognition accuracy. To address this problem, this study proposes an enhanced StarGAN-based denoising method using a single network to handle multiple noise types. Unlike the original StarGAN-VC2 developed for voice conversion, the proposed model is specifically redesigned for leakage voiceprint denoising by integrating MFCC-based representation, a lightweight bottleneck, channel attention, residual feature preservation, and U-Net-style reconstruction. Experimental and engineering application results show that the denoised signals achieve improvements of 3–7 dB in SNR, 3–4 dB in PSNR, and 3–4 in SSR. The model also demonstrates strong generalization capability and plug-and-play applicability, enabling integration with conventional denoising and voiceprint recognition networks. These results indicate that the proposed method can effectively suppress diverse utility tunnel noises while preserving leakage-related voiceprint features. 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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25 pages, 12560 KB  

Open AccessArticle

Edge-Cloud V2X Telemetry Pipeline and Operator Dashboard for Site-Level Supervisory Monitoring of Autonomous Mobile Units in Outdoor Industrial Sites

by Eun-Seong Pak, Bok-Joong Yoon, Kil-Soo Lee, Yong-Chul Cha and Hwa-Young Kim

Appl. Sci. 2026, 16(13), 6682; https://doi.org/10.3390/app16136682 - 3 Jul 2026

Viewed by 376

Abstract

Outdoor industrial sites, including logistics terminals, construction yards, and civil infrastructure worksites, increasingly require supervisory systems for monitoring autonomous mobile units under variable wireless and operational conditions. This study presents an edge-cloud telemetry platform that connects V2X on-board and roadside units to a [...] Read more.

Outdoor industrial sites, including logistics terminals, construction yards, and civil infrastructure worksites, increasingly require supervisory systems for monitoring autonomous mobile units under variable wireless and operational conditions. This study presents an edge-cloud telemetry platform that connects V2X on-board and roadside units to a normalized data pipeline and an operator dashboard. The architecture assigns frame reception and data validation to the edge layer, while cloud services perform stream ingestion, storage, querying, and visualization using a Kafka-Elasticsearch-Grafana stack. A fixed supervisory schema was defined for position, heading, speed, mission state, battery level, and error flags so that virtual fields used in early validation can later be replaced by measured signals without changing downstream interfaces. Physical field validation was conducted using a single test vehicle in a construction-site emulation environment to evaluate communication continuity and dashboard refresh behavior. Multi-unit applicability was examined at the architecture and schema levels, and a preliminary payload-level capacity estimate was derived using the telemetry frequency and payload-length assumptions. Under the tested site conditions, the system maintained continuous reception and visualization over an approximately 700 m distance from the RSU-side reference location. The measured end-to-end display delay averaged 0.78 s, with a standard deviation of 0.059 s and a maximum of 0.96 s. Under a 10 Hz status-message condition, the estimated pure-payload traffic was approximately 23 kbps per mobile unit. These results indicate that V2X-based edge-cloud telemetry can provide a practical baseline for supervisory monitoring in outdoor industrial sites, while simultaneous multi-vehicle validation, detailed network-load evaluation, and long-term field testing remain necessary future work. 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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20 pages, 6641 KB  

Open AccessArticle

Topology-Aware Road Extraction from Remote Sensing Images Using Deep Learning and Graph-Based Connectivity Refinement

by Zixuan Teng, Zezhong Zheng, Xiangyang Sun and Hao Xue

ISPRS Int. J. Geo-Inf. 2026, 15(5), 208; https://doi.org/10.3390/ijgi15050208 - 9 May 2026

Viewed by 1066

Abstract

Road networks are fundamental components of transportation infrastructure and play a crucial role in various geospatial applications. Although deep learning-based semantic segmentation models have achieved promising results in extracting roads from high-resolution remote sensing imagery, the resulting networks often suffer from topological fragmentation [...] Read more.

Road networks are fundamental components of transportation infrastructure and play a crucial role in various geospatial applications. Although deep learning-based semantic segmentation models have achieved promising results in extracting roads from high-resolution remote sensing imagery, the resulting networks often suffer from topological fragmentation due to occlusions and shadows. To address this issue, we propose a topology-aware road extraction method that integrates deep learning-based segmentation with a graph-based connectivity refinement strategy. Specifically, a Pyramid Scene Parsing Network (PSPNet) is first employed to generate initial road probability maps. Subsequently, a connectivity-oriented post-processing pipeline is introduced, which incorporates a multi-source cost function strategy and a direction-aware Dijkstra search algorithm. By utilizing endpoint tangent vectors as inertial weights, the algorithm effectively reconstructs fragmented segments while ensuring geometric smoothness and topological consistency. Furthermore, a dynamic road width restoration strategy is applied to transform refined skeletons into physically consistent road entities. Experiments conducted on two publicly available datasets, CHN6-CUG and DeepGlobe, demonstrate the effectiveness of the proposed method. Quantitative results show that the refinement process significantly enhances road connectivity with a minimal trade-off in pixel-level accuracy. Specifically, the Conn metric increases by 0.1989 on the CHN6-CUG dataset and 0.3055 on the DeepGlobe dataset, while MIoU remains high with only marginal decreases of 1.07% and 0.45%, respectively. These findings indicate that the method effectively restores structural continuity, helping with reliable road network generation and subsequent integration into Geographic Information System (GIS)-based applications such as urban planning and autonomous navigation. 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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22 pages, 5221 KB  

Open AccessArticle

Hybrid Deep Neural Network with Natural Language Processing Techniques to Analyze Customer Satisfaction with Delivery Platform Manager Responses

by Salihah Alotaibi

Appl. Sci. 2026, 16(9), 4359; https://doi.org/10.3390/app16094359 - 29 Apr 2026

Cited by 1 | Viewed by 575

Abstract

Delivery services have drawn much attention and become of topmost significance in urban areas by presenting online food delivery selections for a diversity of dishes from a wide range of restaurants, decreasing both travel and waiting times. Customer data analysis acts as a [...] Read more.

Delivery services have drawn much attention and become of topmost significance in urban areas by presenting online food delivery selections for a diversity of dishes from a wide range of restaurants, decreasing both travel and waiting times. Customer data analysis acts as a cornerstone in corporate strategy, allowing enterprises to gather and interpret user feedback and helping them to make informed decisions that drive future business development. However, major knowledge gaps remain due to the scarcity of literature review studies on these delivery services, hindering a complete understanding of customer satisfaction in this sector. Furthermore, there has been little systematic research on managerial response tactics to online consumer complaints and negative reviews. Researchers have contributed by applying artificial intelligence, including deep learning and machine learning models, to analyze customer sentiment and understand customer brand perceptions. This study presents a Hybrid Deep Neural Network Model for Customer Satisfaction Analysis (HDNNM-CSA), with the aim of developing an efficient model which is capable of accurately classifying customer satisfaction levels in delivery apps based on textual responses provided by customer experience managers. To achieve this, the model initially pre-processes text data using text cleaning, emoji removal, normalization, tokenization, stop word removal, and stemming to clean and standardize the unstructured text data for further analysis. Following this, term frequency–inverse document frequency-based word embedding is utilized to transform the pre-processed text into meaningful feature representations. Lastly, an ensemble architecture involving bidirectional long short-term memory, temporal convolutional, and graph convolutional networks is deployed to classify customer satisfaction levels with managers’ responses. A series of experimental analyses are performed, and the results are examined for numerous features. A comparative analysis demonstrates the enhanced performance of the HDNNM-CSA technique with respect to existing approaches. 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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26 pages, 9517 KB  

Open AccessArticle

SSPRCD: Scene Graph-Based Street-Scene Spatial Positional Relation Change Detection with Graph Differencing and Structural Quantification

by Xian Guo, Wenjing Ding, Yichuan Wang and Jie Jiang

ISPRS Int. J. Geo-Inf. 2026, 15(4), 161; https://doi.org/10.3390/ijgi15040161 - 9 Apr 2026

Viewed by 1100

Abstract

Street-view imagery supports fine-grained urban monitoring, but most street-scene change detection methods are pixel-centric or object-centric and cannot explicitly capture the evolution of inter-entity spatial relations needed for interpretable tasks (e.g., compliance inspection and post-disaster assessment). To address this, we propose SSPRCD, a [...] Read more.

Street-view imagery supports fine-grained urban monitoring, but most street-scene change detection methods are pixel-centric or object-centric and cannot explicitly capture the evolution of inter-entity spatial relations needed for interpretable tasks (e.g., compliance inspection and post-disaster assessment). To address this, we propose SSPRCD, a scene graph-based framework that extracts entity-relation triplets with pixel locations, builds spatial knowledge graphs, and achieves stable node alignment via intra-/inter-temporal consistency. Graph differencing then identifies added, removed, and unchanged entities/relations, while nGED and graph2vec jointly quantify structural discrepancies between temporal scenes. Experiments on the TSUNAMI dataset, with comparisons across two object detectors and seven scene graph generation backbones, show that SSPRCD achieves a macro-F1 of 0.65 for the object-level task, F1 of 0.72 for binary change detection, and F1 of 0.89 for relation-level detection, consistently outperforming baseline methods. Overall, SSPRCD delivers relation-aware and topology-informed change explanations that improve the interpretability of street-block level change analysis for geospatial in-formation updating and urban applications. 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, 4034 KB  

Open AccessArticle

Does GDP Drive Urban Well-Being? Evidence from China’s Urban Physical Examination Survey

by Jincheng Cai and Ju He

ISPRS Int. J. Geo-Inf. 2026, 15(3), 138; https://doi.org/10.3390/ijgi15030138 - 23 Mar 2026

Viewed by 713

Abstract

The relationship between economic development and residents’ perceived urban well-being remains an important question in urban research. This study examines whether the relationship between GDP and city-level satisfaction exhibits non-linear patterns or plateau effects. Using the 2024 nationwide Urban Physical Examination (UPE) resident [...] Read more.

The relationship between economic development and residents’ perceived urban well-being remains an important question in urban research. This study examines whether the relationship between GDP and city-level satisfaction exhibits non-linear patterns or plateau effects. Using the 2024 nationwide Urban Physical Examination (UPE) resident survey in China, this study assesses how city economic level relates to perceived urban well-being, proxied by city-level overall satisfaction. The survey was conducted in April–June 2024 in the main urban districts of 47 cities, using 499,500 valid questionnaires. We aggregate satisfaction to the city level, match it with GDP and key city characteristics, and estimate the GDP–satisfaction association using restricted cubic splines (RCS) to test for potential non-linearity. Across unadjusted and covariate-adjusted models (accounting for population scale and density, industrial structure, fiscal capacity, and regional effects), results show a robust positive association between economic level and satisfaction, while nested-model tests provide no evidence that spline terms improve fit over a linear specification within the observed GDP range. Substantial dispersion around the fitted curve indicates that GDP is an enabling capacity rather than a sufficient condition, pointing to cross-city differences in how effectively resources are converted into lived urban quality. We propose using GDP-adjusted satisfaction benchmarking within the UPE cycle to identify underperforming cities and prioritize targeted governance and renewal actions. 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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28 pages, 4392 KB  

Open AccessArticle

Ontology-Based Decision Support Framework for Urban Road Renewal

by Juan Du, Yimeng Wu, Xiufang Li, Jiaping Hu, Shouqiang Wang and Min Hu

Appl. Sci. 2026, 16(5), 2462; https://doi.org/10.3390/app16052462 - 4 Mar 2026

Viewed by 642

Abstract

Decision-making in Urban Road Renewal is often hindered by the disconnect between static conceptual models and dynamic industry specifications. To address this, this paper proposes an ontology-based decision support framework that formally models and integrates multi-source knowledge for automated compliance checking. A domain [...] Read more.

Decision-making in Urban Road Renewal is often hindered by the disconnect between static conceptual models and dynamic industry specifications. To address this, this paper proposes an ontology-based decision support framework that formally models and integrates multi-source knowledge for automated compliance checking. A domain ontology was constructed by extracting entities from 15 key industry specifications using a BERT-BiLSTM-CRF deep learning model (achieving an accuracy of 98.2%), and a rule base of over 50 Semantic Web Rule Language (SWRL) rules was formulated to enable automated reasoning. The framework’s effectiveness was validated through a multi-agent simulation of the G15 Jialiu renewal project. Results demonstrated that the system-generated optimization measures increased traffic capacity by up to 95.0% and improved the Pavement Condition Index (PCI) by 6.1%, empirically verifying the directional consistency of the decision logic. Finally, the practical feasibility was demonstrated through a Decision Support System (DSS). This research provides a novel framework for leveraging fragmented knowledge, enhancing the consistency and rationality of decision-making in smart city infrastructure 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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25 pages, 7378 KB  

Open AccessArticle

Model Performance Improvement by Accumulated Application of Machine Data in Machine Learning Model for TBM Advance Rate Prediction

by Soon-Wook Choi, Tae-Ho Kang and Soo-Ho Chang

Appl. Sci. 2026, 16(1), 295; https://doi.org/10.3390/app16010295 - 27 Dec 2025

Cited by 1 | Viewed by 894

Abstract

This study quantitatively verified the impact of applying accumulated data on the model’s prediction accuracy, overfitting, and adaptive learning ability by using a method that accumulates and retrains machine data of a TBM generated whenever excavation progresses at regular intervals. To achieve this, [...] Read more.

This study quantitatively verified the impact of applying accumulated data on the model’s prediction accuracy, overfitting, and adaptive learning ability by using a method that accumulates and retrains machine data of a TBM generated whenever excavation progresses at regular intervals. To achieve this, the performance of five machine learning algorithms was evaluated on two field datasets. The best-performing gradient boosting model was selected as the preliminary model. The performance results of the preliminary model and the cumulative model were then compared using another field dataset. The field data for the performance comparison were divided into 14 steps based on ground information, and the performance of the two models was compared sequentially at each step. The results showed that the preliminary and cumulative models exhibited similar predictive performance in the initial intervals. However, the cumulative model more closely matched actual measurements as new data was added than the preliminary model. Consequently, the preliminary model, based on past data, has clear limitations in adapting to the diverse variables encountered in real-world situations. On the other hand, cumulative models are essential for improving real-time prediction performance of processes with constantly changing environments, such as TBM, by continuously increasing relevant field data. 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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24 pages, 5201 KB  

Open AccessArticle

Three-Dimensional Reconstruction of Indoor Building Components Based on Multi-Dimensional Primitive Modeling Method

by Jaeyoung Lee, Soomin Kim and Sungchul Hong

ISPRS Int. J. Geo-Inf. 2026, 15(1), 10; https://doi.org/10.3390/ijgi15010010 - 23 Dec 2025

Viewed by 1147

Abstract

The integration of Building Information Modeling (BIM) and Digital Twin (DT) has emerged as an innovative tool in the architecture, engineering, and construction (AEC) domain. To successfully utilize BIM and DT, it is crucial to update the 3D model in a timely and [...] Read more.

The integration of Building Information Modeling (BIM) and Digital Twin (DT) has emerged as an innovative tool in the architecture, engineering, and construction (AEC) domain. To successfully utilize BIM and DT, it is crucial to update the 3D model in a timely and accurate manner. However, limitations remain when handling massive point clouds to reconstruct complex indoor structures with varying ceiling and floor heights. This study proposes a semi-automatic 3D model reconstruction method. First, point clouds are aligned with 3D Cartesian axes and the spatial extent of the indoor space is measured. Subsequently, the point clouds are projected onto each coordinate plane to hierarchically extract structural elements of a building component, such as boundary lines, rectangles, and cuboids. Boolean operations are then applied to the cuboids to reconstruct a 3D wireframe model. Additionally, wall points are segmented to identify openings like doors and windows. For validation, the method was applied to three typical building components with Manhattan-world structures: an office, a hallway, and a stairway. The reconstructed models were evaluated using reference points, resulting in positional accuracies of 0.033 m, 0.034 m, and 0.030 m, respectively. Finally, the resulting wireframe model served as a reference to build an as-built BIM model. 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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25 pages, 6215 KB  

Open AccessArticle

Advancements Toward a Standard System for Intelligent Operation and Maintenance of Buildings and Municipal Facilities

by Lianzhen Zhang, Yang Hou, Kaizhong Deng and Jiyu Xin

Buildings 2025, 15(21), 3965; https://doi.org/10.3390/buildings15213965 - 3 Nov 2025

Cited by 4 | Viewed by 1600

Abstract

The building and municipal facility sectors in many countries are shifting from rapid construction to a balanced focus on construction and operation & maintenance (O&M). However, O&M practices remain largely manual, with poor digital integration, fragmented data management, and inconsistent performance standards. The [...] Read more.

The building and municipal facility sectors in many countries are shifting from rapid construction to a balanced focus on construction and operation & maintenance (O&M). However, O&M practices remain largely manual, with poor digital integration, fragmented data management, and inconsistent performance standards. The absence of a unified theoretical and standardization framework for intelligent O&M represents a critical research and practice gap. To address this, this paper proposes a comprehensive framework for intelligent O&M standards, grounded in operations management theory and supported by extensive research. The framework is structured across three dimensions: (a) functional services, including perception, data fusion, decision-making, and disaster prevention; (b) system hierarchy, ranging from perception layer and algorithm layer to human–computer interaction layer; and (c) intelligence characteristics, spanning presentation and monitoring to autonomous maintenance. In addition, existing standards and representative applications are reviewed to provide valuable references for the future development of intelligent O&M standard systems. 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, 8186 KB  

Open AccessArticle

The Threshold Effect in the Street Vitality Formation Mechanism

by Yilin Ke, Jiawen Wang, Shiping Lin, Jilong Li, Niuniu Kong, Jie Zeng, Jiacheng Chen and Ke Ai

ISPRS Int. J. Geo-Inf. 2025, 14(11), 417; https://doi.org/10.3390/ijgi14110417 - 24 Oct 2025

Viewed by 1132

Abstract

Street vitality has become a crucial metric for smart city management. Classical theories qualitatively explain that street vitality originates from the dynamic interaction between people and spatial carriers, yet the threshold effect within this process has not been addressed, leaving a gap in [...] Read more.

Street vitality has become a crucial metric for smart city management. Classical theories qualitatively explain that street vitality originates from the dynamic interaction between people and spatial carriers, yet the threshold effect within this process has not been addressed, leaving a gap in urban research. This study selects South China, one of China’s most vibrant and globally influential regions, introduces dissipative structure theory based on classical theories, and constructs a threshold effect hypothesis model for the vitality formation mechanism. Through energy efficiency conversion of data and a slope-based method for identifying balanced time periods, the periods of supply–demand balance in energy efficiency were identified, the threshold effect in vitality formation was captured, and critical thresholds were measured. The results indicate the following: (1) the hypothesis model is valid; (2) the threshold effect is inevitable and periodic, primarily occurring on workdays from 12:00 to 13:00 and 18:00 to 19:00, and on rest days from 08:00 to 09:00 and 18:00 to 19:00; and (3) the activation threshold is quantifiable and exhibits volatility, ranging from 0.40 to 1.56, varying specifically by city, season, day type, and street type. This study advances the translation of street vitality research from theory into practice and provides theoretical support and strategic guidance for smart city management globally, particularly in developing countries. 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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22 pages, 9314 KB  

Open AccessArticle

Reviving and Documenting Architectural Heritage Through Augmented Reality: A New Mobile Interface Experience at the Konya (Türkiye) People’s House

by Osman Ziyaettin Yağcı and Ayşen Esra Bölükbaşı Ertürk

Buildings 2025, 15(17), 3087; https://doi.org/10.3390/buildings15173087 - 28 Aug 2025

Viewed by 1699

Abstract

Traditional methods for documenting cultural heritage often remain inadequate for preserving structural data, making it virtually impossible to archive architectural works that no longer survive. This study investigates the use of augmented reality (AR) technology to improve the sustainability of architectural heritage in [...] Read more.

Traditional methods for documenting cultural heritage often remain inadequate for preserving structural data, making it virtually impossible to archive architectural works that no longer survive. This study investigates the use of augmented reality (AR) technology to improve the sustainability of architectural heritage in the digital environment. The former People’s House (Halkevi) building, once located in Konya, Türkiye but no longer standing, was selected as the case study. Drawing on available photographs and historical documents, a 3D model of the building was generated using Autodesk Revit, further refined in 3ds Max, and transferred to an interactive digital platform via AR applications (ARki, Augmentecture, and a custom AR solution developed with Unity 3D + Vuforia). These applications offer an accessible solution for art and architectural historians thanks to their user-friendly interfaces and the fact that they do not require coding knowledge. Among the tested AR platforms, the Unity + Vuforia-based application yielded the most consistent performance, especially in terms of interactivity, visual stability, and environ-mental integration. The findings indicate that augmented reality can serve as a practical tool for the digital documentation of cultural heritage, demonstrating that researchers without advanced technical expertise can effectively utilize these technologies. This study contributes to digital heritage preservation by proposing a simplified AR-based methodology that reduces the need for cross-disciplinary expertise, enabling wider participation of local stakeholders in the documentation and visualization of lost architectural heritage. 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, 5780 KB  

Open AccessArticle

Utilizing GCN-Based Deep Learning for Road Extraction from Remote Sensing Images

by Yu Jiang, Jiasen Zhao, Wei Luo, Bincheng Guo, Zhulin An and Yongjun Xu

Sensors 2025, 25(13), 3915; https://doi.org/10.3390/s25133915 - 23 Jun 2025

Cited by 3 | Viewed by 2107

Abstract

The technology of road extraction serves as a crucial foundation for urban intelligent renewal and green sustainable development. Its outcomes can optimize transportation network planning, reduce resource waste, and enhance urban resilience. Deep learning-based approaches have demonstrated outstanding performance in road extraction, particularly [...] Read more.

The technology of road extraction serves as a crucial foundation for urban intelligent renewal and green sustainable development. Its outcomes can optimize transportation network planning, reduce resource waste, and enhance urban resilience. Deep learning-based approaches have demonstrated outstanding performance in road extraction, particularly excelling in complex scenarios. However, extracting roads from remote sensing data remains challenging due to several factors that limit accuracy: (1) Roads often share similar visual features with the background, such as rooftops and parking lots, leading to ambiguous inter-class distinctions; (2) Roads in complex environments, such as those occluded by shadows or trees, are difficult to detect. To address these issues, this paper proposes an improved model based on Graph Convolutional Networks (GCNs), named FR-SGCN (Hierarchical Depth-wise Separable Graph Convolutional Network Incorporating Graph Reasoning and Attention Mechanisms). The model is designed to enhance the precision and robustness of road extraction through intelligent techniques, thereby supporting precise planning of green infrastructure. First, high-dimensional features are extracted using ResNeXt, whose grouped convolution structure balances parameter efficiency and feature representation capability, significantly enhancing the expressiveness of the data. These high-dimensional features are then segmented, and enhanced channel and spatial features are obtained via attention mechanisms, effectively mitigating background interference and intra-class ambiguity. Subsequently, a hybrid adjacency matrix construction method is proposed, based on gradient operators and graph reasoning. This method integrates similarity and gradient information and employs graph convolution to capture the global contextual relationships among features. To validate the effectiveness of FR-SGCN, we conducted comparative experiments using 12 different methods on both a self-built dataset and a public dataset. The proposed model achieved the highest F1 score on both datasets. Visualization results from the experiments demonstrate that the model effectively extracts occluded roads and reduces the risk of redundant construction caused by data errors during urban renewal. This provides reliable technical support for smart cities and sustainable development. 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, 11777 KB  

Open AccessArticle

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

Cited by 2 | Viewed by 1957

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

(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, 2166 KB  

Open AccessArticle

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

Cited by 2 | Viewed by 6443

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

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

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20 pages, 1803 KB  

Open AccessArticle

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

Cited by 1 | Viewed by 1370

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

(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, 4838 KB  

Open AccessArticle

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

Cited by 7 | Viewed by 2442

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

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

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24 pages, 8794 KB  

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

Cited by 10 | Viewed by 4734

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 KB  

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

Cited by 1 | Viewed by 2538

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 KB  

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

Cited by 1 | Viewed by 2811

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 KB  

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

Cited by 15 | Viewed by 5134

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 KB  

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 3 | Viewed by 2552

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 KB  

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

Cited by 8 | Viewed by 2583

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 KB  

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

Cited by 3 | Viewed by 4079

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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