Deterioration forecasting of concrete bridge elements in Victoria, Australia using a Markov Chain

Hasan, M; Setunge, Sujeeva; Law, David W.; Hinkeesing, S

doi:10.1201/b17063-121

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…ANNs obtain knowledge through the training phase, which is established by using a set of training data, where the system identifies the relationship between the input and output parameters. The concept of synaptic weights is determined to store the knowledge from training and to use it to solve problems for an unknown dataset [107]. Figure 9 Among these, ANNs have become popular as they overcome the deficiencie rent mechanistic, probabilistic, and statistical models in degradation prediction.…”

Section: Metaheuristic Models For Predictionmentioning

confidence: 99%

Degradation Models and Maintenance Strategies for Reinforced Concrete Structures in Coastal Environments under Climate Change: A Review

Rincon,

Moscoso,

Hamami

et al. 2024

Buildings

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Modern engineering faces challenges in ensuring technical standards for service, durability, and sustainability. Political, administrative, and budgetary factors, coupled with climate change, pose tasks to structural integrity, affecting industries and economies. Marine infrastructures represent a strategic asset of a country as they handle a large part of the economic exchanges. This article analyzes five essential factors that play a fundamental role in the performance analysis of coastal structures: chloride-induced corrosion, degradation models, maintenance strategies, monitoring, and climate change. We start with reinforcement corrosion, which is considered as the main cause of distress, particularly in coastal zones, for the long-term behavior of structures. Additional pressure from the influences of climate change is becoming evident and extreme, leading to a reduction in capacity. To guarantee the lifespan of infrastructures, degradation models contribute by estimating the long-term performance of the asset as a strategic piece to the development of effective maintenance solutions. Artificial Neural Networks (ANNs) have gained recent prominence in this field due to their ability to learn intricate patterns from historical data, making them valuable instruments for predicting structural deterioration. Additionally, quantifying the condition of the structure from monitoring data plays a crucial part in providing information on the current situation of the structure. Finally, this review summarizes the challenges associated with the maintenance of aging marine structures considering aspects such as corrosion, monitoring, and the future challenges this area will face due to climate change.

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Section: Metaheuristic Models For Predictionmentioning

confidence: 99%

Degradation Models and Maintenance Strategies for Reinforced Concrete Structures in Coastal Environments under Climate Change: A Review

Rincon,

Moscoso,

Hamami

et al. 2024

Buildings

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“…Environmental factors were also considered in deterioration models in Australia. In addition to age, ADT, ADTT, and structure type were adopted by Callow [11], and the chloride and sulphate content were included in the ANN deterioration model developed by Hasan [67].…”

Section: Factors Considered In Other Countriesmentioning

confidence: 99%

Identifying the Factors Impacting Bridge Deterioration in the Gulf Cooperation Council

Al-Rashed,

Abdelfatah,

Yehia

2023

Designs

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The deterioration module (DM) is one of the four major modules necessary for any bridge management system (BMS). Environmental conditions, structural systems, bridge configuration, geographic location, and traffic data are some of the major factors that affect the development of deterioration modules. This emphasizes the need for the development of deterioration models that reflect the local conditions. In this article, some of the most important factors that could help in developing deterioration models in the Gulf Cooperation Council (GCC) were identified. The research was conducted in three phases; in the first phase, an extensive literature search was conducted to identify factors adopted in different deterioration models, and in phase two, the most relevant factors to the GCC environment were selected and these factors were further reduced based on input from local bridge experts. The result from the second phase is a list of factors identified by the experts. The identified list was utilized in phase three, which was focused on conducting a survey targeting bridge engineers to help identify the final selection and rank the factors according to their importance level. The results indicate that steel reinforcement protection, design load, chloride attack, type of defect, and age are the most important factors impacting bridge deterioration in the GCC. In addition, the time of rehabilitation; average daily truck traffic, ADTT; and average daily traffic, ADT, are the second most important factors. Factors with medium importance level are deck protection, services under the bridge, and inspection gap. The least important set of factors include temperature and wind load.

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Deterioration forecasting of concrete bridge elements in Victoria, Australia using a Markov Chain

Cited by 2 publications

References 2 publications

Degradation Models and Maintenance Strategies for Reinforced Concrete Structures in Coastal Environments under Climate Change: A Review

Degradation Models and Maintenance Strategies for Reinforced Concrete Structures in Coastal Environments under Climate Change: A Review

Identifying the Factors Impacting Bridge Deterioration in the Gulf Cooperation Council

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