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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 6428

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Special Issue Editors

Dr. Jianfeng Zhao

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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

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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

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Guest Editor

Department of Construction and Real Estate, Southeast University, Nanjing, China
Interests: infrastructure resilience modeling; infrastructure resilience simulation; human-based resilience
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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

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Keywords

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

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Published Papers (9 papers)

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Research

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29 pages, 3251 KiB

Open AccessArticle

Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions

by Khaled M. Salem, Javier M. Rey-Hernández, A. O. Elgharib and Francisco J. Rey-Martínez

Appl. Sci. 2025, 15(12), 6806; https://doi.org/10.3390/app15126806 - 17 Jun 2025

Viewed by 301

Abstract

Industry 5.0 is transforming energy demand by integrating sustainability into energy planning, ensuring market stability while minimizing environmental impact for future generations. There are several patterns for calculating energy consumption depending on whether it is measured daily, monthly, or annually through the integration of artificial intelligence approaches, particularly Artificial Neural Networks (ANNs) and Random Forests (RFs), and within the framework of Industry 5.0. This study employs machine learning techniques to analyze energy consumption data from two distinct buildings in Spain: the LUCIA facility in Valladolid and the FUHEM Building in Madrid. The implementation was conducted using custom MATLAB code developed in-house. Our approach systematically evaluates and compares the predictive performance of Artificial Neural Networks (ANNs) and Random Forests (RFs) for energy demand forecasting, leveraging each algorithm’s unique characteristics to assess their suitability for this application. The performances of both models are calculated using the Root Mean Square Percentage Error (RMSPE), Root Mean Square Relative Percentage Error (RMSRPE), Mean Absolute Percentage Error (MAPE), Mean Absolute Relative Percentage Error (MARPE), Kling–Gupta Efficiency (KGE), and also the coefficient of determination, R². Training times are validated using ANN and RF models. Lucia RF took 2.8 s, while Lucia ANN took 40 s; FUHEM RF took 0.3 s, compared to FUHEM ANN, which took 1.1 s. The performances of the two models are described in detail to show the effectiveness of each of them. Full article

(This article belongs to the Special Issue Infrastructure Resilience Analysis)

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27 pages, 8922 KiB

Open AccessArticle

Assessing Building Seismic Exposure Using Geospatial Technologies in Data-Scarce Environments: Case Study of San José, Costa Rica

by Javier Rodríguez-Saiz, Beatriz González-Rodrigo, Juan Gregorio Rejas-Ayuga, Diego A. Hidalgo-Leiva and Miguel Marchamalo-Sacristán

Appl. Sci. 2025, 15(11), 6318; https://doi.org/10.3390/app15116318 - 4 Jun 2025

Viewed by 396

Abstract

The world population affected by seismic risk is increasing due to urban sprawl, especially in vulnerable areas of countries with high population growth. Despite this trend, seismic exposure assessments have predominantly focused on cities in high-income countries, leaving a knowledge gap in data-scarce, seismically active urban areas. This research presents a novel, scalable geospatial methodology for seismic exposure assessment in contexts with limited data availability and its application to San José, Costa Rica, evaluating its time and cost efficiency. The methodology prioritizes the use of free and open-access geospatial data to construct city-scale Geospatial Exposure Databases (city-GEDs) at the individual building level. These databases integrate key attributes from the Global Earthquake Model (GEM) taxonomy, including the building footprint, the plan regularity, the construction date, the roof material, the relative position within the urban block, and urban block compactness. Random Forest classification models were developed to assign buildings to expert-defined building typologies (BTs). In the case of San José, 7226 buildings were classified into eight typologies using the derived attributes, achieving a classification error of 46%. When the building height—visually sampled—was included, the error decreased significantly to 13%, confirming its importance in typology prediction and emphasizing the need for efficient acquisition strategies. This approach is essential for quick pre- or post-disaster seismic risk assessment, allowing time and cost-effective data collection and analysis. This contribution is particularly relevant for Central America and other seismically active regions with limited data, supporting improved risk analysis and urban resilience planning. Full article

(This article belongs to the Special Issue Infrastructure Resilience Analysis)

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24 pages, 7394 KiB

Open AccessArticle

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 418

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 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

Open AccessArticle

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 850

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 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

Open AccessArticle

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 394

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 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

Open AccessArticle

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 651

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 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

Open AccessArticle

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

Cited by 3 | Viewed by 1545

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 (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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Review

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22 pages, 1291 KiB

Open AccessReview

Small but Significant: A Review of Research on the Potential of Bus Shelters as Resilient Infrastructure

by Sarah Briant, Debra Cushing, Tracy Washington and Monique Swart

Appl. Sci. 2025, 15(12), 6724; https://doi.org/10.3390/app15126724 - 16 Jun 2025

Viewed by 268

Abstract

Bus stops are an essential component of public transportation systems, significantly impacting human health, wellbeing, and overall user experience. As primary interaction points for passengers, they are integral to the urban landscape and, as such, their designs influence people’s experiences within the public realm. Despite their importance, the design of bus stops and bus shelters remains an under-researched area. This paper aims to review the existing peer-reviewed research on bus-stop design, identifying areas for future inquiry. Twenty-two peer-reviewed journal articles were selected and included in this study. The most common theme in the published research was the manner in which bus stops could address extreme weather and heat, along with other themes, including accessibility, sustainable energy, air pollution, and noise. Further empirical research is necessary to understand how bus-stop design affects the user experience, emphasizing qualitative methods to explore human experiences, perceptions, motivations, and challenges related to bus-stop usage and public transportation. Full article

(This article belongs to the Special Issue Infrastructure Resilience Analysis)

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27 pages, 2899 KiB

Open AccessReview

Review on Soft Mobility Infrastructure Design Codes

by Chang Chen, Zoi Christoforou and Nadir Farhi

Appl. Sci. 2025, 15(12), 6406; https://doi.org/10.3390/app15126406 - 6 Jun 2025

Viewed by 275

Abstract

Soft mobility is gaining popularity in urban spaces due to its various benefits in terms of carbon footprint, air quality, congestion mitigation, and public health. Soft mobility infrastructure mainly includes urban road adjustments to accommodate pedestrian and bicycle flows. Relevant design codes are being developed worldwide, and important investments are being made in soft mobility. This paper provides a review and comparative analysis of 17 design codes and regulations from different countries and regions across the world. Furthermore, the German road design code for motorized traffic is used as a reference to assess the level of detail and eventual gaps in the soft mobility infrastructure design codes. Results indicate that, in contrast to road codes, soft mobility infrastructure codes vary significantly from country to country. Most importantly, the limit and recommended values of geometric parameters are fewer in number and less documented compared to road design parameters. Evidence-based recommendations are needed to enhance the design, construction, operation, maintenance, and safe management of soft mobility infrastructure. Full article

(This article belongs to the Special Issue Infrastructure Resilience Analysis)

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