Fragility analysis of bridges due to overweight traffic load

Fiorillo, Graziano; Ghosn, Michel

doi:10.1080/15732479.2017.1380675

Cited by 24 publications

(12 citation statements)

References 25 publications

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“…Moreover, and not surprisingly, these findings were in line with those discussed in the literature. Indeed, it has been shown that the likelihood of bridge failure increases with the percentage of overweight trucks [ 67 ], especially when they cross the bridge simultaneously (e.g. Refs.…”

Section: Resultsmentioning

confidence: 99%

“…Refs. [ 24 , 25 ], and [ 67 ]). Consequently, compensation fees for overweight trucks have been recommended to balance the economic costs associated with reduced bridge life [ 68 ].…”

Section: Resultsmentioning

confidence: 99%

“…Refs. [ 24 , 25 , 67 ], and [ 68 ]). Hence, there is little doubt as to how crucial it is that RAs exercise greater vigilance before granting permits for overloaded vehicles.…”

Section: Resultsmentioning

confidence: 99%

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Prediction of the severity of exceeding design traffic loads on highway bridges

Ventura,

Barabino,

Maternini

2024

Heliyon

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Section: Resultsmentioning

confidence: 99%

“…Refs. [ 24 , 25 ], and [ 67 ]). Consequently, compensation fees for overweight trucks have been recommended to balance the economic costs associated with reduced bridge life [ 68 ].…”

Section: Resultsmentioning

confidence: 99%

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Prediction of the severity of exceeding design traffic loads on highway bridges

Ventura,

Barabino,

Maternini

2024

Heliyon

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“…For instance, the EU fleet registered 3.9 million goods vehicles in 2020 (7.3% more than by 2019), from which heavy vehicles with maximum laden weights between 10.1 to 20 tonnes represented between 70 to 97% the total road freight transport across Europe [1]. This trend has raised growing concern among transportation agencies over the contribution of overweight vehicles to accelerating the deterioration rates of pavements and bridges as well as the associated increases in maintenance, upgrading, and replacement costs of highway infrastructures [2]. For instance, 17 bridge collapses (10.83% the total number of collapses) directly related to overweight vehicles were registered in China since 2000 [3].…”

Section: Introductionmentioning

confidence: 99%

Self-powered weigh-in-motion system combining vibration energy harvesting and self-sensing composite pavements

Birgin

García‐Macías

D’Alessandro

et al. 2023

Construction and Building Materials

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“…where h ¼ À2.88, ln is the natural logarithm of the number in the curly bracket, w is a normalized OW value between 0 and 1 from a cumulative normal distribution with OW mean equal to 48% and standard deviation equal to 9%. Similarly, a is a normalized ADTT value between 0 and 1 from a cumulative normal distribution with mean equal to 5,630 trucks per day and standard deviation equal to 4,520 (23). The ADTT is collected from the NBI database, whereas the percentage of overweight trucks traveling in New Jersey is obtained using WIM data.…”

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confidence: 99%

Development of a Risk Assessment Module for Bridge Management Systems in New Jersey

Fiorillo

Nassif

2020

Transportation Research Record: Journal of the Transportation R

Self Cite

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Bridges are critical for the mobility of our society and its economic growth. Available funds for bridge repair, maintenance, and rehabilitation are limited. The Moving Ahead for Progress in the 21st Century Act (MAP-21) introduced several new parameters for improving the management of bridge assets, such as bridge element evaluation, life-cycle analysis, and risk-based performance indicators. Risk-based methods account for the uncertainties embedded into engineering variables and long-term evaluations. The objective of this paper is to identify, assess, and quantify structural risk components to bridges using probabilistic risk methodologies and data from the National Bridge Inventory database. The aim is to simplify the implementation of risk-based ranking procedures into bridge management system packages according to the MAP-21 vision. Therefore, machine learning techniques are employed to facilitate the introduction of probabilistic risk methods into bridge management systems. The procedure is described for seven hazards that are pertinent to bridges in New Jersey: overloading, fatigue, seismic, flooding, scour, vehicle and vessel collision. Risk values are computed in monetary terms to homogenize the comparison among bridges for different hazards. The analysis is performed on 5,534 bridges, showing that seismic events and fatigue resulting from truck overloading are the most dominant hazards in New Jersey, for which about 97.0% and 29.0% of bridges show some level of risk. The main limitation of the proposed framework is the lack of accurate data from bridge inventories necessary to thoroughly perform a fully structural probabilistic analysis of bridges and to minimize engineering judgment.

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Fragility analysis of bridges due to overweight traffic load

Cited by 24 publications

References 25 publications

Prediction of the severity of exceeding design traffic loads on highway bridges

Prediction of the severity of exceeding design traffic loads on highway bridges

Self-powered weigh-in-motion system combining vibration energy harvesting and self-sensing composite pavements

Development of a Risk Assessment Module for Bridge Management Systems in New Jersey

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