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Infrastructure Resilience Analysis
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Infrastructure Resilience Analysis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 4150

Special Issue Editors

Dr. Jianfeng Zhao

E-Mail Website
Guest Editor
Architecture and Built Environment Department, Northumbria University, Newcastle upon Tyne, UK
Interests: infrastructure resilience; infrastructure finance; project management; project value; digitalisation
Dr. Henry Liu

E-Mail Website
Guest Editor
School of Design and the Built Environment, University of Canberra, Bruce, ACT, Australia
Interests: transport infrastructure resilience; performance measurement; infrastructure procurement; decision making; workforce planning
Prof. Dr. Jingfeng Yuan

E-Mail Website
Guest Editor
Department of Construction and Real Estate, Southeast University, Nanjing, China
Interests: infrastructure resilience modeling; infrastructure resilience simulation; human-based resilience
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Infrastructure, such as transport and water networks, forms the backbone of economies and societies worldwide. However, climate change-induced and other system failures are increasingly disrupting their functionality, highlighting the necessity and significance of resilient infrastructure. These assets' complexity, growing interdependence, and diverse functions and stakeholders often exacerbate this situation. Acknowledging this urgent need, this Special Issue aims to bring together a comprehensive collection of scholarly papers tackling key infrastructure resilience aspects to achieve future-proof infrastructure assets.

The scope of this Special Issue includes but is not limited to:

  • Taxonomy (e.g., concepts and contexts) of infrastructure resilience;
  • Performance measures/ frameworks for assessing infrastructure resilience;
  • Optimization approaches to improving infrastructure resilience;
  • Data, risks, and uncertainties in managing infrastructure resilience;
  • Case studies that examine practices in infrastructure resilience;
  • Socioeconomic perspectives of infrastructure resilience (e.g., impacted communities such as 15-minute neighborhoods, stakeholders such as strengthening stakeholder participation and collaboration for adaptive pathways to resilient infrastructure, demand, finance, and governance).

Dr. Jianfeng Zhao
Dr. Henry Liu
Prof. Dr. Jingfeng Yuan
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Applied Sciences 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 2400 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

  • taxonomy
  • performance measures
  • frameworks
  • optimization approaches
  • risks and uncertainties
  • case studies
  • socio-economic impacts

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Published Papers (5 papers)

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Research

24 pages, 7394 KiB  
Article
Measurements of High-Froude Number Boat Wakes near a Seawall
by Steven D. Meyers, Stacey Day and Mark E. Luther
Appl. Sci. 2025, 15(9), 4807; https://doi.org/10.3390/app15094807 - 26 Apr 2025
Viewed by 294
Abstract
Characterizing the coastal wave environment, typically composed of wind-driven waves and boat wakes, and its interaction with built infrastructure is essential for planning sustainable and resilient shoreline development and protection. Objectively identifying and measuring non-stationary wave features, particularly boat wakes, in longer data [...] Read more.
Characterizing the coastal wave environment, typically composed of wind-driven waves and boat wakes, and its interaction with built infrastructure is essential for planning sustainable and resilient shoreline development and protection. Objectively identifying and measuring non-stationary wave features, particularly boat wakes, in longer data records remains a challenge. A wave gauge array of four pressure sensors was deployed for several weeks in the northernmost section of urbanized Tampa Bay, FL, a sheltered, shallow (mean depth 1.2 m) region with frequent recreational small-boat activity. New methods for analyzing these measurements were explored. The array had a square geometry, allowing the calculation of directional spectra. Most prior studies of boat wakes could only examine amplitude spectra. A nearby seawall was found to be a significant source of wave reflection. Additionally, a novel empirical method for identifying wakes, distinguishing them from wind-driven waves, and providing an estimate of their duration and amplitude was developed. The method was found to reliably identify most primary wakes but not reflected wakes. Reflected boat wakes were identified manually, and only during times of relatively high water levels when the shoreline in front of the seawall was flooded. Full article
(This article belongs to the Special Issue Infrastructure Resilience Analysis)
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24 pages, 1663 KiB  
Article
Improving Cost Contingency Estimation in Infrastructure Projects with Artificial Neural Networks and a Complexity Index
by Michael C. P. Sing, Qiuwen Ma and Qinhuan Gu
Appl. Sci. 2025, 15(7), 3519; https://doi.org/10.3390/app15073519 - 24 Mar 2025
Viewed by 600
Abstract
Machine learning (ML) algorithms have been developed for cost performance prediction in the form of single-point estimates where they provide only a definitive value. This approach, however, often overlooks the vital influence project complexity exerts on estimation accuracy. This study addresses this limitation [...] Read more.
Machine learning (ML) algorithms have been developed for cost performance prediction in the form of single-point estimates where they provide only a definitive value. This approach, however, often overlooks the vital influence project complexity exerts on estimation accuracy. This study addresses this limitation by presenting ML models that include interval predictions and integrating a complexity index that accounts for project size and duration. Utilizing a database of 122 infrastructure projects from public works departments totaling HKD 5465 billion (equivalent to USD 701 billion), this study involved training and evaluating seven ML algorithms. Artificial neural networks (ANNs) were identified as the most effective, and the complexity index integration increased the R2 for ANN-based single-point estimation from 0.808 to 0.889. In addition, methods such as bootstrapping and Monte Carlo dropout were employed for interval predictions, which resulted in significant improvements in prediction accuracy when the complexity index was incorporated. These findings not only advance the theoretical framework of ML algorithms for cost contingency prediction by implementing interval predictions but also provide practitioners with improved ML-based tools for more accurate infrastructure project cost performance predictions. Full article
(This article belongs to the Special Issue Infrastructure Resilience Analysis)
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20 pages, 4857 KiB  
Article
From Battlefield to Building Site: Probabilistic Analysis of UXO Penetration Depth for Infrastructure Resilience
by Boules N. Morkos, Magued Iskander, Mehdi Omidvar and Stephan Bless
Appl. Sci. 2025, 15(6), 3259; https://doi.org/10.3390/app15063259 - 17 Mar 2025
Cited by 1 | Viewed by 314
Abstract
Remediation of formerly used war zones requires knowledge of the depth of burial (DoB) of unexploded ordnances (UXOs). The DoB can vary greatly depending on soil and ballistic conditions, and their associated uncertainties. In this study, the well-known physics-based Poncelet equation is used [...] Read more.
Remediation of formerly used war zones requires knowledge of the depth of burial (DoB) of unexploded ordnances (UXOs). The DoB can vary greatly depending on soil and ballistic conditions, and their associated uncertainties. In this study, the well-known physics-based Poncelet equation is used to set a framework for stochastic prediction of the DoB of munitions in sandy, clayey sand, and clayey sediments using Monte Carlo simulations (MCSs). First, the coefficients of variation (COVs) of the empirical parameters affecting the model were computed, for the first time, from published experimental data. Second, the behavior of both normal and lognormal distributions was investigated and it was found that both distributions yielded comparable DoB predictions for COVs below 30%. However, a lognormal distribution was preferred, to avoid negative value sampling, since COVs of the studied parameters can easily exceed this threshold. Third, the performance of several MCS sampling techniques, including the Pseudorandom Generator (PRG), Latin Hypercube Sampling (LHS), and Gaussian Process Response Surface Method (GP_RSM), in predicting the DOB was explored. Different probabilistic sampling techniques produced similar DoB predictions for each soil type, but GP_RSM was the most computationally efficient method. Finally, a sensitivity analysis was conducted to determine the contribution of each random variable to the predicted DoB. Uncertainty of the density, drag coefficient, and bearing coefficient dominated the DoB in sandy soil, while uncertainty in the bearing coefficient controlled DoB in clayey sand soils. In clayey soil, all variables under various distribution conditions resulted in approximately identical predictions, with no single variable appearing to be dominant. It is recommended that Monte Carlo simulations using GP_RSM sampling from lognormally distributed effective variables be used for predicting DoB in soils with high COVs. Full article
(This article belongs to the Special Issue Infrastructure Resilience Analysis)
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20 pages, 4190 KiB  
Article
Assessing Community-Level Flood Resilience: Analyzing Functional Interdependencies Among Building Sectors
by Yang Lu, Guanming Zhang and Donglei Wang
Appl. Sci. 2025, 15(6), 3161; https://doi.org/10.3390/app15063161 - 14 Mar 2025
Viewed by 483
Abstract
This study presents a comprehensive framework for evaluating community-level flood resilience by integrating the fragility of individual buildings, the functionality of critical infrastructure sectors, and their interdependencies. Using performance-based engineering principles, the framework quantifies resilience through isolated building fragility curves, sector-specific functionality fragility [...] Read more.
This study presents a comprehensive framework for evaluating community-level flood resilience by integrating the fragility of individual buildings, the functionality of critical infrastructure sectors, and their interdependencies. Using performance-based engineering principles, the framework quantifies resilience through isolated building fragility curves, sector-specific functionality fragility curves, and a synthesized community-level functionality model. Applied to a virtual community of 1000 archetypal buildings, the analysis reveals that community functionality decreases with increasing flood depth, reaching a critical threshold of 0.87 at 1.57 m. The sensitivity analysis underscores the importance of accounting for intersectoral dependencies, as they significantly influence community-wide functionality. The results highlight the residential sector’s dominant role in shaping resilience and its cascading effects on other sectors. This framework provides actionable insights for planners and stakeholders, emphasizing the need to prioritize interventions in sectors with the highest vulnerability and dependency to enhance disaster preparedness and response strategies. This framework, novel in its integration of building-level fragility curves with community-wide intersectoral dependencies, provides actionable insights for planners and stakeholders, emphasizing targeted interventions in vulnerable sectors to enhance flood resilience. Full article
(This article belongs to the Special Issue Infrastructure Resilience Analysis)
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16 pages, 2426 KiB  
Article
Decarbonizing Near-Zero-Energy Buildings to Zero-Emission Buildings: A Holistic Life Cycle Approach to Minimize Embodied and Operational Emissions Through Circular Economy Strategies
by Amalia Palomar-Torres, Javier M. Rey-Hernández, Alberto Rey-Hernández and Francisco J. Rey-Martínez
Appl. Sci. 2025, 15(5), 2670; https://doi.org/10.3390/app15052670 - 1 Mar 2025
Viewed by 1291
Abstract
The decarbonization of the building sector is essential to mitigate climate change, aligning with the EU’s Energy Performance of Buildings Directive (EPBD) and the transition from near-Zero-Energy Buildings (nZEBs) to Zero-Emission Buildings (ZEBs). This study introduces a novel and streamlined Life Cycle Assessment [...] Read more.
The decarbonization of the building sector is essential to mitigate climate change, aligning with the EU’s Energy Performance of Buildings Directive (EPBD) and the transition from near-Zero-Energy Buildings (nZEBs) to Zero-Emission Buildings (ZEBs). This study introduces a novel and streamlined Life Cycle Assessment (LCA) methodology, in accordance with EN 15978, to holistically evaluate the Global Warming Potential (GWP) of buildings. Our approach integrates a calibrated dynamic simulation of operational energy use, performed with DesignBuilder, to determine precise operational CO2 emissions. This is combined with a comprehensive assessment of embodied emissions, encompassing construction materials and transportation phases, using detailed Environmental Product Declarations (EPDs). Applied to the IndUVa nZEB case study, the findings reveal that embodied emissions dominate the life cycle GWP, accounting for 69%, while operational emissions contribute just 31% over 50 years. The building’s use of 63.8% recycled materials highlights the transformative role of circular economy strategies in reducing embodied impacts. A comparative analysis of three energy-efficiency scenarios demonstrates the IndUVa building’s exceptional performance, achieving energy demand reductions of 78.4% and 85.6% compared to the ASHRAE and CTE benchmarks, respectively. This study underscores the growing significance of embodied emissions as operational energy demand declines. Achieving ZEBs requires prioritizing embodied carbon reduction through sustainable material selection, recycling, and reuse, targeting a minimum of 70% recycled content. By advancing the LCA framework, this study presents a pathway for achieving ZEBs, driving a substantial reduction in global energy consumption and carbon emissions, and contributing to climate change mitigation. Full article
(This article belongs to the Special Issue Infrastructure Resilience Analysis)
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