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Buildings
Special Issues
Reliability, Resilience and Sustainability for Construction and Infrastructure Systems
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Reliability, Resilience and Sustainability for Construction and Infrastructure Systems

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 4184

Special Issue Editors

Prof. Dr. Zhenhao Zhang

E-Mail Website
Guest Editor
School of Civil Engineering, Changsha University of Science and Technology, Changsha 410114, China
Interests: dynamic reliability; structural reliability; stochastic process; random vibration; uncertainty analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Yue-Ling Long

E-Mail Website
Guest Editor
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Interests: FRP–concrete–steel composite structures; steel–concrete composite structures; concrete-filled steel tubes; stainless steel structures; bamboo structures; cross-section instability
Special Issues, Collections and Topics in MDPI journals
Dr. Dong Li

E-Mail Website
Guest Editor
College of Civil Engineering, Fuzhou University, Fuzhou 350806, China
Interests: marine corrosion and protection; coastal infrastructure maintenance; anti-corrosion coating
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern construction and infrastructure systems face unprecedented challenges due to climate change, urbanization, aging assets, and increasing interdependencies. Ensuring infrastructure reliability (consistent performance under normal conditions), resilience (ability to withstand and recover from disruptions), and sustainability (long-term environmental, economic, and social viability) is critical for the transformation of the construction industry. This Special Issue aims to explore cutting-edge research, innovative technologies, and integrated strategies that leverage digital tools, artificial intelligence, and advanced management practices to enhance the reliability, resilience, and sustainability of infrastructure systems, including buildings, transportation, energy, water, and smart cities.

We welcome original research, case studies, and review articles addressing topics including, but not limited to, the following:

  • Advanced analysis methods: New reliability analysis methods, resilience analysis methods, and sustainability evaluation methods.
  • Structural Reliability: Predictive maintenance, failure analysis, probabilistic risk assessment, and lifecycle performance optimization.
  • Resilience Engineering: Adaptive design, disaster preparedness, rapid recovery strategies, and stress-testing infrastructure under extreme events.
  • Sustainable Infrastructure: Low-carbon materials, circular economy applications, energy-efficient systems, and green infrastructure solutions.
  • Climate Adaptation and Mitigation: Infrastructure hardening, nature-based solutions, and decarbonization strategies for resilient and sustainable systems.
  • Policy, Governance, and Socioeconomic Factors: Regulatory frameworks, investment models, multi-stakeholder collaboration, and equity considerations in infrastructure planning.
  • Systemic Risks and Interdependencies: Cascading failures, critical infrastructure interdependencies, and network-wide resilience optimization.

This Special Issue encourages interdisciplinary contributions from engineering, environmental science, data science, economics, and policy studies. It seeks to provide actionable insights for researchers, practitioners, and policymakers to develop infrastructure that is reliable under daily demands, resilient to shocks, and sustainable for future generations.

Prof. Dr. Zhenhao Zhang
Dr. Yue-Ling Long
Dr. Dong Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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

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

Keywords

  • structural reliability
  • structural resilience
  • infrastructure sustainability
  • advanced analysis methods
  • quantitative evaluation

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

Published Papers (5 papers)

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Research

23 pages, 1981 KB  
Article
Forecasting Fatal Construction Accidents Using an STL–BiGRU Hybrid Framework: A Multi-Scale Time Series Approach
by Yuntao Cao, Rui Zhang, Ziyi Qu, Martin Skitmore, Xingguan Ma and Jun Wang
Buildings 2026, 16(8), 1539; https://doi.org/10.3390/buildings16081539 - 14 Apr 2026
Viewed by 298
Abstract
Accurate forecasting of fatal construction accidents is critical for proactive safety management; however, accident time series exhibit strong non-stationarity, nonlinear dynamics, and multi-scale temporal patterns that challenge conventional models. This study proposes a hybrid STL–BiGRU framework that integrates Seasonal–Trend decomposition using Loess (STL) [...] Read more.
Accurate forecasting of fatal construction accidents is critical for proactive safety management; however, accident time series exhibit strong non-stationarity, nonlinear dynamics, and multi-scale temporal patterns that challenge conventional models. This study proposes a hybrid STL–BiGRU framework that integrates Seasonal–Trend decomposition using Loess (STL) with a Bidirectional Gated Recurrent Unit (BiGRU) network to deliver robust and interpretable forecasts tailored to construction safety needs. STL first decomposes the original monthly accident series (January 2012–December 2024, OSHA) into trend, seasonal, and residual components, reducing structural complexity and mitigating non-stationarity. Independent BiGRU models are then trained on each component to capture bidirectional temporal dependencies, and final forecasts are reconstructed through component aggregation. Comparative experiments against Gated Recurrent Units (GRUs), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), Support Vector Regression (SVR), Autoregressive Integrated Moving Average (ARIMA), and their STL-enhanced variants demonstrate that the proposed STL–BiGRU model achieves superior performance across both short-term and medium-term horizons. The model achieves the lowest error levels, with a short-term Root Mean Squared Error (RMSE) of 6.8522 and a medium-term RMSE of 7.0568, and shows consistent improvements in Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Results indicate that multi-scale decomposition combined with bidirectional deep learning provides a practical, forward-looking tool. It helps regulators and contractors anticipate high-risk periods, optimize resource allocation, and reduce fatal accidents through targeted preventive measures. Full article
(This article belongs to the Special Issue Reliability, Resilience and Sustainability for Construction and Infrastructure Systems)
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22 pages, 2601 KB  
Article
Study on Maximum Vertical Prestressing Spacing for Long-Span PC Continuous Rigid-Frame Bridges
by Fei Xia, Shenxin Zhang and Yasir Ibrahim Shah
Buildings 2026, 16(7), 1363; https://doi.org/10.3390/buildings16071363 - 30 Mar 2026
Viewed by 388
Abstract
Vertical prestressing is critical for shear resistance in long-span PC continuous rigid-frame bridges, yet existing design methods rely on the inaccurate superposition of single-tendon stress fields, neglecting mechanical interaction between adjacent tendons. This study derives the first closed-form elastic analytical solution for the [...] Read more.
Vertical prestressing is critical for shear resistance in long-span PC continuous rigid-frame bridges, yet existing design methods rely on the inaccurate superposition of single-tendon stress fields, neglecting mechanical interaction between adjacent tendons. This study derives the first closed-form elastic analytical solution for the vertical normal stress field under two interacting prestressing tendons, explicitly capturing the coupling term. Validated against high-fidelity Finite Element Analysis (FEA), the solution achieves a Mean Absolute Percentage Error (MAPE) below 6.8%, outperforming conventional superposition methods by 6.8–17.7 percentage points. The analysis reveals a transition from diffusion-dominated to superposition-dominated stress regimes and establishes a predictive linear relationship between tendon spacing and the depth of the prestressing blind zone. The section at one-fourth of the web height below the top edge is identified as the critical control section, leading to a proposed maximum spacing limit of 0.34 times the web height to ensure a stress uniformity coefficient greater than 0.95. This criterion represents a 13.3% increase over empirical rules and a 27.5% increase over the JTG 3362-2018 limit, enabling estimated savings of 52,000 CNY per typical four-span bridge while maintaining structural safety. This represents a 13.3% increase over empirical rules and a 27.5% increase over the limit in JTG 3362-2018, enabling estimated savings of 52,000 CNY per typical four-span bridge while maintaining structural safety. Full article
(This article belongs to the Special Issue Reliability, Resilience and Sustainability for Construction and Infrastructure Systems)
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33 pages, 8018 KB  
Article
Ground Settlement Susceptibility Assessment in Urban Areas Using PSInSAR and Ensemble Learning: An Integrated Geospatial Approach
by WoonSeong Jeong, Moon-Soo Song, Sang-Guk Yum and Manik Das Adhikari
Buildings 2025, 15(23), 4364; https://doi.org/10.3390/buildings15234364 - 2 Dec 2025
Cited by 2 | Viewed by 988
Abstract
Ground settlement is a multifaceted geological phenomenon driven by natural and man-made forces, posing a significant impediment to sustainable urban development. Thus, ground settlement susceptibility (GSS) mapping has emerged as a critical tool for understanding and mitigating cascading hazards in seismically active and [...] Read more.
Ground settlement is a multifaceted geological phenomenon driven by natural and man-made forces, posing a significant impediment to sustainable urban development. Thus, ground settlement susceptibility (GSS) mapping has emerged as a critical tool for understanding and mitigating cascading hazards in seismically active and anthropogenically modified sedimentary basins. Here, we develop an integrated framework for assessing GSS in the Pohang region, South Korea, by integrating Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR)-derived vertical land motion (VLM) data with seismological, geotechnical, and topographic parameters (i.e., peak ground acceleration (PGA), effective shear-wave velocity (Vs30), site period (Ts), general amplification factor (AF), seismic vulnerability index (Kg), soil depth, topographic slope, and landform classes) through ensemble machine learning models such as Random Forest (RF), XGBoost, and Decision Tree (DT). Analysis of 56 Sentinel-1 SLC images (2017–2023) revealed persistent subsidence concentrated in Quaternary alluvium, reclaimed coastal plains, and basin-fill deposits. Among the tested models, RF achieved the best performance and strongly agreed with field evidence of sand boils, liquefaction, and structural damage from the 2017 Pohang earthquake. The very-high-susceptibility zones exhibited mean subsidence rates of −3.21 mm/year, primarily within soft sediments (Vs30 < 360 m/s) and areas of thick alluvium deposits. Integration of the optimal RF-based GSS index with regional building inventories revealed that nearly 65% of existing buildings fell within high- to very-high-susceptibility zones. The proposed framework demonstrates that integrating PSInSAR and ensemble learning provides a robust and transferable approach for quantifying ground settlement hazards and supporting risk-informed urban planning in seismically active and complex geological coastal environments. Full article
(This article belongs to the Special Issue Reliability, Resilience and Sustainability for Construction and Infrastructure Systems)
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23 pages, 2011 KB  
Article
A Second-Order Second-Moment Approximate Probabilistic Design Method for Structural Components Considering the Curvature of Limit State Surfaces
by Hanmin Liu, Yicheng Mao, Zhenhao Zhang, Fang Yuan and Fuming Wang
Buildings 2025, 15(18), 3421; https://doi.org/10.3390/buildings15183421 - 22 Sep 2025
Cited by 1 | Viewed by 912
Abstract
The current engineering structural design code employs a direct probability design method based on the Taylor series expansion of the performance function at verification points, retaining only linear terms. This approach ignores the curvature and other nonlinear properties of the performance function, leading [...] Read more.
The current engineering structural design code employs a direct probability design method based on the Taylor series expansion of the performance function at verification points, retaining only linear terms. This approach ignores the curvature and other nonlinear properties of the performance function, leading to insufficient accuracy. To address the deficiencies of the current design method, this paper develops an approximate probability design method that considers the curvature of the limit state surface, integrating it with the second-order moment theory based on the direct probability design method. Using a simply supported plate as a representative example, this paper systematically compares the performance of the proposed design method with the direct probability design method, the partial coefficient method, and the design value method in reinforcement design. The reinforcement areas calculated by the four methods are similar, confirming the correctness and practicality of the proposed method for engineering applications. The accuracy of the design outcomes from the various methods is validated through Monte Carlo simulation. The results indicate that the method proposed in this paper exhibits a high accuracy, with the relative errors of the reliability indices in the two examples being 0.346% and 0.228%, respectively—significantly lower than those of the direct probability design method (2.919% and 0.769%, respectively). This underscores the effectiveness and substantial benefits of the proposed method in structural reliability design, offering a dependable, highly accurate, and economically viable design tool for engineering applications. Full article
(This article belongs to the Special Issue Reliability, Resilience and Sustainability for Construction and Infrastructure Systems)
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18 pages, 5480 KB  
Article
Experimental Study on Performance of High-Performance Concrete Based on Different Fine Aggregate Systems
by Xiaojun He, Enjin Zhu, Mingxiang Zhang, Liao Wu and Peiguo Li
Buildings 2025, 15(18), 3386; https://doi.org/10.3390/buildings15183386 - 18 Sep 2025
Cited by 3 | Viewed by 1072
Abstract
To advance the adoption of manufactured sand, this study investigated concrete mix designs wherein manufactured sand partially substituted natural river sand and fully replaced fine aggregates. The influences of the water–binder ratio and fly ash content were also examined. Experimental findings indicate that [...] Read more.
To advance the adoption of manufactured sand, this study investigated concrete mix designs wherein manufactured sand partially substituted natural river sand and fully replaced fine aggregates. The influences of the water–binder ratio and fly ash content were also examined. Experimental findings indicate that at replacement rates of 50% and 70%, the workability and mechanical properties of mixed sand concrete experienced a decline. The mechanical performance of concrete improved as the water–binder ratio decreased. Additionally, the strength properties of manufactured sand concrete initially increased with higher fly ash content but slightly decreased when fly ash content reached 30%. Nevertheless, all strength metrics still satisfied the design specifications. Thus, the overall performance of high-performance concrete incorporating manufactured sand remains favorable, demonstrating its viability as a full replacement for river sand in concrete production. Full article
(This article belongs to the Special Issue Reliability, Resilience and Sustainability for Construction and Infrastructure Systems)
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