A Big Data Approach for Investigating Bridge Deterioration and Maintenance Strategies in Taiwan

Chuang, Yu-Han; Yau, Nie-Jia; Tabor, John Mark M.

doi:10.3390/su15021697

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…This helps maximize the return on investment for public infrastructure projects and ensures that bridges continue to serve their intended purpose for an extended period. Bridge inspections are crucial for several reasons, including ensuring public safety, maintaining structural integrity, identifying repair and maintenance needs, prolonging the bridge's lifespan, and facilitating efficient resource allocation [17][18][19][20]. Regular inspections help identify and address potential issues before they become critical, ensuring the safety of all bridge users, prolonging the lifespan, and enabling efficient resource allocation for maintenance and repairs [21].…”

Section: Area Key Reasonmentioning

confidence: 99%

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Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Shahin,

Chen,

Maghanaki

et al. 2024

Sensors

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Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.

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Section: Area Key Reasonmentioning

confidence: 99%

“…Maintenance and repair [19] Bridge inspections help identify areas that require maintenance or repairs, allowing authorities to address problems before they become critical. Regular maintenance can help avoid costly and time-consuming repairs or replacements.…”

Section: Area Key Reasonmentioning

confidence: 99%

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Shahin,

Chen,

Maghanaki

et al. 2024

Sensors

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“…In particular, the information stored in the RDB is automatically made available in GIS and allows the automatic generation of preliminary BIM models, which can be enriched with multi-source data derived from the use of the latest technologies. These include (i) sensing techniques, e.g., sensors [25,26] and laser scanners [27][28][29], useful in the phase of geometric characterization of the artefact and identification of its state of damage; (ii) space-based techniques such as interferometric synthetic aperture radar (InSAR) [30][31][32][33], enabling the measurement of millimetre settlements of the structure and the souring area; and (iii) analytical models, such as mechanical models [34,35], machine learning models [36][37][38], and multi-level monitoring algorithms [39][40][41][42]. Compared to existing literature, the methodology intends to enrich BIM models and GIS representation with information on the defect level of individual structural elements of a bridge and the attention class of the entire structure according to an algorithm in which the relational DB plays a central role.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Data-Driven System for Digital Bridge Management

Pallante,

Meriggi,

D’Amico

et al. 2024

Buildings

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Relational databases are established and widespread tools for storing and managing information. The efficient collection of information in a database appears to be a promising solution for bridge management (BM), thus facilitating the digital transition. The Italian regulatory framework on infrastructure operation and maintenance (O&M) is complex and is constantly being updated. The current plan for implementing its guidelines envisages that infrastructure managers, also on a regional scale, equip themselves with their own digital database for BM. Within this context, this research proposes an integrated methodology that collects information derived from project documentation, in situ inspections, digital surveys, and monitoring and field tests in a queryable database for digitalising, georeferencing, and creating models of many bridges. Structured query language (SQL) statements are used to efficiently export specific shared information, enabling network cross-analysis. Furthermore, the database represents the source of a geographic information system (GIS) catalogue and the basis for deriving models for building information modelling (BIM). The methodology focuses on the infrastructural context of the Lazio region, Italy, the first beneficiary of the research.

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“…Bridge deterioration characteristics are important bases for formulating maintenance strategies. Existing studies have investigated the overall deterioration patterns of bridges, such as segment linear and nonlinear deterioration models [2], cluster analysis models [3], and deterioration models under human intervention and after unexpected events [4]. However, there has been no research focus on the deterioration of bridge superstructures, substructures, or deck systems according to the actual inspection data of multiple bridges.…”

Section: Introductionmentioning

confidence: 99%

A Decision-Making Method for Bridge Network Maintenance Based on Disease Transmission and NSGA-II

Shen

Liu

et al. 2023

Sustainability

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Due to the unreasonable allocation method, the bridge maintenance funds in Shaanxi Province can not provide maximum value. In this study, we used different functions to fit the actual technical condition data of bridges in Shaanxi in recent years, and through comparison, the exponential model with the best fitting effect was selected as the deterioration model for the bridge and its parts. Based on this, the deterioration characteristics of the bridge and its parts were further analyzed. Based on the results of the analysis, the deterioration coefficients (A) applicable to the bridge and its parts in Shaanxi Province, considering the ages and service environments of the bridges, are summarized. The concept of disease transmission is proposed based on the different deterioration characteristics of the bridge parts. The deck pavement, expansion joints, and bearings were identified as the key components of disease transmission, and the disease transmission paths were built with them as the center. Considering the technical benefits, economic benefits, and diseases of the key components of bridge maintenance, the second-generation nondominated sorting genetic algorithm (NSGA-II) was used to calculate the maintenance fund allocation scheme in the bridge network maintenance decision under different budget–demand ratios. The findings show that there are significant differences in the deterioration rates of the deck systems, superstructures, and substructures, as well as of the overall bridges, in Northern Shaanxi, Central Shaanxi (Guanzhong region), and Southern Shaanxi. Diseases of the bridge deck pavement, expansion joints, and bearings can lead to diseases of other elements. Compared with the traditional method of allocating maintenance funds based on the principle of prioritizing bridges with the worst technical conditions, the economic condition growth rates of the results of this study were 94.2%, 106.2%, 92.9%, and 62.8% for budget–demand ratios of 20%, 40%, 60%, and 80% of the maintenance funds, respectively. The proposed method for allocating bridge maintenance funds provides a reference for the subsequent development of bridge maintenance decision specifications.

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A Big Data Approach for Investigating Bridge Deterioration and Maintenance Strategies in Taiwan

Cited by 5 publications

References 29 publications

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

An Integrated Data-Driven System for Digital Bridge Management

A Decision-Making Method for Bridge Network Maintenance Based on Disease Transmission and NSGA-II

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