Resilient Civil Infrastructure, 2nd Edition

Abstract submission deadline

31 July 2026

Manuscript submission deadline

31 October 2026

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Topic Information

Dear Colleagues,

This topic is “Resilient Civil Infrastructure”, which has proven to be successful in the past (https://www.mdpi.com/topics/RCI). Due to their vital role in modern communities and cities, civil infrastructures should be able to resist and recover from natural or human-made disasters such as earthquakes, hurricanes, floods, tsunamis, fires, blasts, etc. Developing a resilient civil infrastructure has garnered significant research attention over the last decade. Although significant advances have been made in this field in recent years, there are still important challenges related to more effective resilience quantification and the resilience enhancement of civil infrastructures to multiple disasters, ranging from the theory aspect (e.g., mechanical principle, interaction effect) to the technology aspect (e.g., material property, system innovation) and the decision aspect (e.g., assessment strategy, decision making). These challenges require further, more comprehensive efforts and more general intervention planning. From the above perspective, this topic aims to improve knowledge and performance in resilient civil infrastructure through enhanced scientific and multi-disciplinary works. The potential topics include (but are not limited to): Methodology for resilience assessment and quantification; Probabilistic theory and method for resilient infrastructure; Resilient construction materials; Innovative resilient structures; Multiple-hazard effects on resilience; Resilient community and smart city; Structural resilience and service life extension; Design optimization for resilient structure; Resilient management and performance improvement; Interaction between resilient structures and environment.

Prof. Dr. De-Cheng Feng
Dr. Ji-Gang Xu
Dr. Xu-Yang Cao
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400	Submit
Buildings buildings	3.1	3.4	2011	17.2 Days	CHF 2600	Submit
Designs designs	-	3.9	2017	15.2 Days	CHF 1600	Submit
Infrastructures infrastructures	2.7	5.2	2016	16.8 Days	CHF 1800	Submit
Journal of Marine Science and Engineering jmse	2.7	4.4	2013	16.9 Days	CHF 2600	Submit

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Published Papers (3 papers)

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All Applied Sciences Buildings Designs Infrastructures JMSE

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18 pages, 7247 KiB  

Open AccessArticle

Intelligent Inspection Method for Rebar Installation Quality of Reinforced Concrete Slab Based on Point Cloud Processing and Semantic Segmentation

by Ruishi Wang, Jianxiong Zhang, Hongxing Qiu and Jian Sun

Buildings 2024, 14(11), 3693; https://doi.org/10.3390/buildings14113693 - 20 Nov 2024

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Abstract

The rebar installation quality significantly impacts the safety and durability of reinforced concrete (RC) structures. Traditional manual inspection is time-consuming, inefficient, and highly subjective. In order to solve this problem, this study uses a depth camera and aims to develop an intelligent inspection [...] Read more.

The rebar installation quality significantly impacts the safety and durability of reinforced concrete (RC) structures. Traditional manual inspection is time-consuming, inefficient, and highly subjective. In order to solve this problem, this study uses a depth camera and aims to develop an intelligent inspection method for the rebar installation quality of an RC slab. The Random Sample Consensus (RANSAC) method is used to extract point cloud data for the bottom formwork, the upper and lower rebar lattices, and individual rebars. These data are utilized to measure the concrete cover thickness, the distance between the upper and lower rebar lattices, and the spacing between rebars in the RC slab. This paper introduces the concept of the “diameter calculation region” and combines point cloud semantic information with rebar segmentation mask information through the relationship between pixel coordinates and camera coordinates to measure the nominal diameter of the rebar. The verification results indicate that the maximum deviations for the concrete cover thickness, the distance between the upper and lower rebar lattices, and the spacing of the double-layer bidirectional rebar in the RC slab are 0.41 mm, 1.32 mm, and 5 mm, respectively. The accuracy of the nominal rebar diameter measurement reaches 98.4%, demonstrating high precision and applicability for quality inspection during the actual construction stage. Overall, this study integrates computer vision into traditional civil engineering research, utilizing depth cameras to acquire point cloud data and color results. It replaces inefficient manual inspection methods with an intelligent and efficient approach, addressing the challenge of detecting double-layer reinforcement. This has significant implications for practical engineering applications and the development of intelligent engineering monitoring systems. Full article

(This article belongs to the Topic Resilient Civil Infrastructure, 2nd Edition)

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14 pages, 3811 KiB  

Open AccessArticle

Recovery Resiliency Characteristics of Interdependent Critical Infrastructures in Disaster-Prone Areas

by Partha Sarker, Bhushan Lohar, Sean Walker, Stephanie Patch and John T. Wade

Infrastructures 2024, 9(11), 208; https://doi.org/10.3390/infrastructures9110208 - 19 Nov 2024

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Abstract

When Hurricane Maria struck the island of Puerto Rico in September, 2017, it devastated the island’s critical infrastructures, including the well-documented total loss of electric power systems. The strong interdependencies or associations among critical infrastructures in modern society meant that the failure of [...] Read more.

When Hurricane Maria struck the island of Puerto Rico in September, 2017, it devastated the island’s critical infrastructures, including the well-documented total loss of electric power systems. The strong interdependencies or associations among critical infrastructures in modern society meant that the failure of power systems propagated to and exacerbated the failure of other infrastructure systems. Moreover, these associations impact systems recovery just as they impact system failure. This study is a follow-up of previous research by the first author on Hurricane Maria. In this research authors extracted and quantified the recovery associations of Hurricane Fiona (September 2022) made landfall in Puerto Rico and inflicted considerable damage to its critical infrastructures. The recovery efforts following the disaster provided an opportunity to follow up on the previous research and examine the recovery associations. Significant money and efforts have gone into upgrading the infrastructures of Puerto Rico to make them more resilient to natural disasters such as hurricanes or tropical storms following Hurricane Maria. This paper explores the new recovery resiliency characteristics of Puerto Rico’s critical infrastructure systems (CISs) that the recovery efforts following Hurricane Fiona illustrate. This research shows that the power systems and other CISs of Puerto Rico are much more resilient when compared to their state of resiliency in 2017. Moreover, examining the recovery interdependencies reveals that some of the CISs are strongly dependent on power systems recovery. Outcomes of this study suggest that CIS relationships based on recovery data from Puerto Rico, are transferable to similar disaster-prone areas such as the Caribbean islands or other island nations, as they have similar characteristics and challenges. Full article

(This article belongs to the Topic Resilient Civil Infrastructure, 2nd Edition)

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21 pages, 5643 KiB  

Open AccessArticle

Study on the Effect of Heat Transfer Characteristics of Energy Piles

by Xiaoyang Wang, Tongyu Xu, Kaiming Zhao, Yueqiu Xia, Yuechen Duan, Weijun Gao and Gangqiang Kong

Buildings 2024, 14(11), 3593; https://doi.org/10.3390/buildings14113593 - 12 Nov 2024

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Abstract

The thermal performance of energy piles equipped with new metal fins to improve heat transmission is examined in this research. The solid heat transfer module of COMSOL Multiphysics was used to create a 2D numerical model of the energy pile, utilizing the energy [...] Read more.

The thermal performance of energy piles equipped with new metal fins to improve heat transmission is examined in this research. The solid heat transfer module of COMSOL Multiphysics was used to create a 2D numerical model of the energy pile, utilizing the energy pile at a field test site in Nanjing as an example. By contrasting the experimental data, the COMSOL Multiphysics model’s correctness was confirmed. After that, a new kind of energy pile fin was created to improve the heat transfer of the pile. The impact of the new fin type on the energy pile’s heat transfer efficiency was assessed, and the temperature change within the soil surrounding the pile before and after the fin was set was examined by contrasting the parameters of pipe configuration, buried pipe depth, and concrete thermal conductivity. The results indicate that after setting the fins to run for 336 h, the temperature of the concrete area increases by 10.8% to 12.3%, and the temperature of the region surrounding the pile increases by 5.3% to 8.7% when the tube diameter is chosen to be between 20 and 40 mm; The fins maximize the heat transfer temperature between the surrounding soil and the concrete, and as the tube diameter increases, the temperature drops. For 336 h of pile operation, the temperature of the concrete may be raised by 10.8% to 12.3% after the fins are set, and the temperature around the pile can be raised by 5.3% to 8.7%. The heat transmission efficiency of the energy pile can be improved by raising the temperature of the soil surrounding the pile through an increase in the concrete’s thermal conductivity; however, the degree of improvement diminishes as the conductivity rises. It is intended that this study will offer insightful information on the best way to design energy pile heat transfer efficiency. Full article

(This article belongs to the Topic Resilient Civil Infrastructure, 2nd Edition)

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