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Monitoring, Assessment, and Interventions on Existing Bridges: From Physical Methods to Machine Learning-Based Applications

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

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 7366

Special Issue Editors


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Department of Architecture, Università degli Studi Roma Tre, 00153 Rome, Italy
Interests: seismic design of structures; assessment and retrofitting of existing bridges and structures; soil structure interaction
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Civil and Environmental Engineering, University of Wisconsin-Milwaukee, 3200 N Cramer Street, Milwaukee, WI 53211, USA
Interests: structural engineering; bridge engineering; durability; reliability; repair and rehabilitation; cable-stayed bridges; jointless bridges; integral abutment bridges; survival analysis
Special Issues, Collections and Topics in MDPI journals

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Department of Structural and Geotechnical Engineering, Faculty of Civil and Industrial Engineering, Sapienza University of Rome, 00184 Rome, Italy
Interests: structural monitoring; structural control; structural concrete
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
College of Civil Engineering, Fuzhou University, Fuzhou 350108, China
Interests: optimal design of structures; rehabilitation exisiting strctures and infrastructures; durability; steel-concrete composite structures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Transportation networks are essential for the mobility of persons and goods as well as for economic, social, and territorial cohesion. Unfortunately, several critical infrastructures are poorly maintained, and some of them are unable to meet current or near-future demands. Since transportation systems suffer from deterioration phenomena while the mobility demand intensifies due to growing volumes of exchanges, it has become increasingly evident that there is an urgent need to evaluate the reliability of existing infrastructures, so as to ensure safety, satisfactory quality of service, and proper prioritization of suitable actions such as retrofitting, partial closures, or even dismission. As key elements in most roadway and railway transportation networks, bridges deserve special attention and call for effective monitoring, assessment, and intervention strategies. Within this framework, parallel to recent advances in classical physical methods, a growing interest in the use of smart technologies and machine learning-based tools can also be noted. In this perspective, the present Special Issue is meant at collecting original contributions on the following topics:

  • Static or dynamic experimental testing and structural monitoring of existing bridges, with the design or implementation of smart technologies being especially welcome;
  • Static or dynamic numerical procedures for the structural assessment of existing bridges, taking into special account the occurrence of deterioration phenomena or concurrent multiple hazards;
  • Structural identification and diagnosis of bridges, with focus on the application and validation of machine learning-based techniques;
  • Laboratory or in situ tests as well as numerical modeling or analytical design of retrofitting interventions for existing bridges.

Prof. Dr. Camillo Nuti
Prof. Dr. Habib Tabatabai
Prof. Dr. Giuseppe Quaranta
Prof. Dr. Bruno Briseghella
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.

Published Papers (3 papers)

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Research

21 pages, 4983 KiB  
Article
A Comparative Study of a Fully-Connected Artificial Neural Network and a Convolutional Neural Network in Predicting Bridge Maintenance Costs
by Chongjiao Wang, Changrong Yao, Siguang Zhao, Shida Zhao and Yadong Li
Appl. Sci. 2022, 12(7), 3595; https://doi.org/10.3390/app12073595 - 1 Apr 2022
Cited by 3 | Viewed by 2124
Abstract
The cost assessment of bridge maintenance is a difficult topic to study, but it is critical for a bridge life cycle cost analysis. The maintenance costs sample database was established in this study according to actual engineering data, and a bridge maintenance cost [...] Read more.
The cost assessment of bridge maintenance is a difficult topic to study, but it is critical for a bridge life cycle cost analysis. The maintenance costs sample database was established in this study according to actual engineering data, and a bridge maintenance cost prediction model was developed using a fully-connected artificial neural network (ANN) and convolutional neural network (CNN), respectively. First, eight main factors affecting maintenance costs were evaluated based on the random forest method, and the evaluation results were verified by an exploratory data analysis. The original data were then screened based on the isolation forest principle, and the recent gross domestic product (GDP) growth rate was used to illustrate the relationship between economic development and bridge maintenance costs. Finally, these two neural networks were used to establish maintenance cost prediction models, respectively. The results from the two models were compared and their prediction accuracies were analyzed. The prediction performance of the CNN model for bridge maintenance costs was found to be better than that of the traditional fully-connected ANN model. The results of this study will enhance the opportunity for bridge managers to balance lifecycle maintenance costs. Full article
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19 pages, 6289 KiB  
Article
Geometrical Parametric Study on Steel Beams Exposed to Solar Radiation
by Sallal R. Abid, Thaar S. Al-Gasham, Junqing Xue, Yongjian Liu, Jiang Liu and Bruno Briseghella
Appl. Sci. 2021, 11(19), 9198; https://doi.org/10.3390/app11199198 - 2 Oct 2021
Cited by 7 | Viewed by 1796
Abstract
A finite element thermal analysis was conducted in this study with the aim of evaluating the influence of the geometrical parameters of steel sections on their thermal response under solar radiation. Four W12 and W24 standard steel beams were investigated under the solar [...] Read more.
A finite element thermal analysis was conducted in this study with the aim of evaluating the influence of the geometrical parameters of steel sections on their thermal response under solar radiation. Four W12 and W24 standard steel beams were investigated under the solar irradiation conditions of a sunny summer day. The finite element analysis was carried out using COMSOL Multiphysics considering the Sun’s movement from sunrise to sunset, reflected radiation from the ground, surface convection of air and long wave radiation as the main boundary thermal loads. The temperature-time variation at different locations in the sections, vertical temperature distributions, temperature gradient distributions and thermal stress distributions were investigated. The results showed that the daily maximum temperatures, temperature variation, temperature and temperature gradient distributions and thermal stresses are influenced by the geometry of the steel section. The flange width and flange thickness were found to be the controlling parameters during the noon hours, while these parameters in addition to web depth control the shading effect during the afternoon. On the other hand, web thickness affects the temperature of webs at sunrise and sunset times. Geometrical ratios like Wf/H, Wf/tf2 and 2Wf/Htf were the most influential parameters on temperatures, temperature gradients and thermal stresses of steel beams subjected to solar radiation. The investigated section with the maximum Wf/tf2 value of 0.96 (W12 × 58) recorded the highest top-surface noon temperature, while section W24 × 84 with the lowest Wf/tf2 value of 0.60 exhibited the lowest temperature. Full article
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33 pages, 7982 KiB  
Article
Performance-Based Ranking of Existing Road Bridges
by Ana Mandić Ivanković, Dominik Skokandić, Marija Kušter Marić and Mladen Srbić
Appl. Sci. 2021, 11(10), 4398; https://doi.org/10.3390/app11104398 - 12 May 2021
Cited by 5 | Viewed by 2563
Abstract
Bridge condition assessment in most European countries is based on visual inspection in combination with damage assessment of bridge components. For adequate bridge management, the assessment needs to be further developed to move from the bridge component level to the system functionality level [...] Read more.
Bridge condition assessment in most European countries is based on visual inspection in combination with damage assessment of bridge components. For adequate bridge management, the assessment needs to be further developed to move from the bridge component level to the system functionality level and finally to the priority ranking level for repairs in the network. Although visual inspection provides only qualitative insights into bridge condition and cannot predict load-carrying capacity, it is still very often the only way to collect data on existing bridges and can provide very important information for evaluating structural safety, traffic safety, durability, and overall bridge condition. Therefore, this paper presents a unique procedure that establishes a relationship between a country-specific bridge condition assessment procedure based on visual inspection and the systematization of key bridge performance indicators developed within the European integrated management approach at three complementary and interrelated levels—component, system, and network levels. The assessment procedure for existing bridges initiates with damage assessment based on visual inspection of bridge components and runs through weighting at component, system, and network levels to the six most important key performance indicators (KPIs) for road bridges, which are organized as graphical and numerical inputs for ranking priority maintenance. These are bridge condition assessment, structural safety, traffic safety, durability indicator, availability, and the importance of the bridge in the network. The procedure is validated on a case study set of five real bridges, using the decision-making process as an example for the small sample size. The case study bridges differ in cross-section, type, and span (which vary from 9.5 to 72 m). The bridges were built between 1958 and 2001 and are located either on state or municipal roads in Croatia. The results, in terms of condition classification and priorities of future interventions within the representative group of bridges, justify the application of the described assessment procedure. Additional digitization efforts could easily implement the described assessment approach at the infrastructure network level. Full article
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