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

Abstract

<jats:p> 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. </jats:p>

Traffic Hazards on Main Road’s Bridges: Real-Time Estimating and Managing the Overload Risk

“…The literature provides contributions along three main directions according to the objective of the proposed analysis. The first direction is numerically predominant and assumes traffic load hazard as a component of a broader multi-hazard risk assessment framework (e.g., [17], [18], [19], [20]). For example, [17] proposed an approach for identifying, assessing, and quantifying risk on bridges resulting from numerous hazards, such as overload, fatigue, earthquake, storm surges, scour, vehicle collisions, and vessel collisions.…”

“…It employed generalized extreme-value distributions, as observed in [18] and [28]. Occasionally, the RAM was integrated with fragility curves, describing the probability of failure as a function of hazard intensity ( [2], [17], [21]). Fragility curves for the overload hazard were calculated using numerical simulations based on 3D non-linear grillage Finite Element Models (FEMs) of the [21]) or through analytical formulations that considered the average daily truck traffic, the percentage of overweight trucks, and bridge deterioration ( [17]), for example.…”

“…For (a), the estimation of increased vehicular operating costs and users' travel time involved multiplying traffic volumes passing on the bridges and detour lengths by some parameters (e.g., average detour speed, average vehicular occupancies, marginal operating costs, marginal value of time). Additionally, costs related to injuries and fatalities were estimated based on statistics computed from past bridge failure events (e.g., [17], [18], [28], [20]). Concerning (b), the computation of increased vehicular operating costs and users' travel time involved developing Transportation Network Models (TNMs) that simulated network damage states, as in [2], [22], [24], [31], and [32].…”