A Review of Probabilistic Methods of Assessment of Load Effects in Bridges

This paper reviews a range of statistical approaches to illustrate the influence of data quality and quantity on the probabilistic modelling of traffic load effects. It also aims to demonstrate the importance of long-run simulations in calculating characteristic traffic load effects. The popular methods of Peaks Over Threshold and Generalized Extreme Value are considered but also other methods including the Box-Cox approach, fitting to a Normal distribution and the Rice formula. For these five methods, curves are fitted to the tails of the daily maximum data. Bayesian Updating and Predictive Likelihood are also assessed, which require the entire data for fittings. The accuracy of each method in calculating 75-year characteristic values and probability of failure, using different quantities of data, is assessed. The nature of the problem is first introduced by a simple numerical example with a known theoretical answer. It is then extended to more realistic problems, where long-run simulations are used to provide benchmark results, against which each method is compared. Increasing the number of data in the sample results in higher accuracy of approximations but it is not able to completely eliminate the uncertainty associated with the extrapolation. Results also show that the accuracy of estimations of characteristic value and probabilities of failure are more a function of data quality than extrapolation technique. This highlights the importance of long-run simulations as a means of reducing the errors associated with the extrapolation process

Simulation of Traffic Loading on Highway Bridges

This work is based on weigh-in-motion measurements for approximately three million trucks obtained from sites in five European countries. Techniques have been developed, supported by photographic evidence, for filtering the measurements to identify and remove unreliable values, and for the classification of extremely heavy vehicles. The collected measurements have been used as the basis for building and calibrating a Monte Carlo simulation model for bridge loading. Two-lane traffic is simulated – either two lanes in the same direction or one lane in each direction. The model allows for vehicles that are both heavier and have more axles than in the measured data. Careful program design and optimisation have made it practical to simulate thousands of years of traffic. This has a number of advantages – the variability associated with extrapolation is greatly reduced, rare events are modelled, and the simulation output identifies the typical loading scenarios which produce the lifetime maximum loading. Analysis of the measured data shows subtle patterns of correlation in vehicle weights and gaps, both within lanes and between adjacent lanes in same-direction traffic. A new approach has been developed for simulating traffic in two same-direction lanes using flow-dependent traffic scenarios. The measured weights and gaps in the scenarios are modified using variable-bandwidth kernel density estimators. This method is relatively simple to apply and can be extended to more than two lanes. It is shown that the correlation structure in the traffic has a small but significant effect on characteristic maximum loading

An Enhanced Bridge Weigh-in-motion Methodology and A Bayesian Framework for Predicting Extreme Traffic Load Effects of Bridges

In the past few decades, the rapid growth of traffic volume and weight, and the aging of transportation infrastructures have raised serious concerns over transportation safety. Under these circumstances, vehicle overweight enforcement and bridge condition assessment through structural health monitoring (SHM) have become critical to the protection of the safety of the public and transportation infrastructures. The main objectives of this dissertation are to: (1) develop an enhanced bridge weigh-in-motion (BWIM) methodology that can be integrated into the SHM system for overweight enforcement and monitoring traffic loading; (2) present a Bayesian framework to predict the extreme traffic load effects (LEs) of bridges and assess the implication of the growing traffic on bridge safety. Firstly, an enhanced BWIM methodology is developed. A comprehensive review on the BWIM technology is first presented. Then, a novel axle detection method using wavelet transformation of the bridge global response is proposed. Simulation results demonstrate that the proposed axle detection method can accurately identify vehicle axles, except for cases with rough road surface profiles or relatively high measurement noises. Furthermore, a two-dimensional nothing-on-road (NOR) BWIM algorithm that is able to identify the transverse position (TP) and axle weight of vehicles using only weighing sensors is proposed. Results from numerical and experimental studies show that the proposed algorithm can accurately identify the vehicle’s TP under various conditions and significantly improve the identification accuracy of vehicle weight compared with the traditional Moses’s algorithm. Secondly, a Bayesian framework for predicting extreme traffic LEs of bridges is presented. The Bayesian method offers a natural framework for uncertainty quantification in parameter estimation and thus can provide more reliable predictions compared with conventional methods. A framework for bridge condition assessment that utilizes the predicted traffic LEs is proposed and a case study on the condition assessment of an instrumented field bridge is presented to demonstrate the proposed methodology. Moreover, the non-stationary Bayesian method is adopted to predict the maximum traffic LEs during the lifetime of bridges subject to different types of traffic growth and the influence of the traffic growth on the bridge safety is investigated

Finding the Distribution of Bridge Lifetime Load Effect by Predictive Likelihood

To assess the safety of an existing bridge, the loads to which it may be subject in its lifetime are required. Statistical analysis is used to extrapolate a sample of load effect values from the simulation period to the required design period. Complex statistical methods are often used and the end result is usually a single value of characteristic load effect. Such a deterministic result is at odds with the underlying stochastic nature of the problem. In this paper, predictive likelihood is shown to be a method by which the distribution of the lifetime extreme load effect may be determined. A basic application to the prediction of lifetime Gross vehicle Weight (GVW) is given. Results are also presented for some cases of bridge loading, compared to a return period approach and important differences are identified. The implications for the assessment of existing bridges are discussed

Highway Bridge Traffic Loading

In this chapter, traditional approaches and recent advances in highway bridge traffic loading are described, which are of great significance for structural safety assessment of bridges. Indeed, it is widely accepted that consideration of site-specific traffic features can enable significant savings in maintenance operations. While short spans are governed by free-flowing traffic plus an allowance for the dynamic effects, long spans are governed by congested conditions. For the former, a promising research trend is the investigation of the dynamic vehicle-bridge interaction, which is shown to lead to dynamic effects much lower than previously thought. For the latter, advances in traffic flow modelling enable the simulation of realistic congestion patterns based on widely available free-flowing traffic data, thus partially overcoming a long-standing shortage of congestion data. Here, emphasis is given to the promising application of traffic microsimulation to long-span bridge loading, combined with a probabilistic approach based on the extreme value theory, to compute site-specific characteristic loading values

Performance Measures to Assess Resiliency and Efficiency of Transit Systems

Transit agencies are interested in assessing the short-, mid-, and long-term performance of infrastructure with the objective of enhancing resiliency and efficiency. This report addresses three distinct aspects of New Jersey’s Transit System: 1) resiliency of bridge infrastructure, 2) resiliency of public transit systems, and 3) efficiency of transit systems with an emphasis on paratransit service. This project proposed a conceptual framework to assess the performance and resiliency for bridge structures in a transit network before and after disasters utilizing structural health monitoring (SHM), finite element (FE) modeling and remote sensing using Interferometric Synthetic Aperture Radar (InSAR). The public transit systems in NY/NJ were analyzed based on their vulnerability, resiliency, and efficiency in recovery following a major natural disaster

Probalistic analysis of highway bridge traffic loading

Many bridges of the world’s highway networks have been in service for decades and are subject to escalating volumes of traffic. Consequently, there is a growing need for the rehabilitation or replacement of bridges due to deterioration and increased loading. The assessment of the strength of the existing bridge is relatively well understood, whereas the traffic loading it is subject to, is not as well understood. Accurate assessment of the loading to which bridges may be subject, can result in significant savings for the highway maintenance budgets internationally. In recent years, a general approach has emerged in the research literature: the characteristics of the traffic at a site are measured and used to investigate the load effects to which the bridge may be subject in its remaining lifetime. This research has the broad objective of developing better methods of statistical analysis of highway bridge traffic loading. The work focuses on short- to medium-length (approximately 15 to 50 m), single- or two-span bridges with two opposing lanes of traffic. Dynamic interaction of the trucks on the bridge is generally not included. Intuitively, it can be accepted that the gap between successive trucks has important implications for the amount of load that may be applied to any given bridge length. This work describes, in quantitative terms, the implications for various bridge lengths and load effects. A new method of modelling headway for this critical time-frame is presented. When daily maximum load effects (for example) are considered as the basis for an extreme value statistical analysis of the simulation results, it is shown that although this data is independent, it is not identically distributed. Physically this is manifest as the difference in load effect between 2- and 3-truck crossing events. A method termed composite distribution statistics is presented which accounts for the different distributions of load effect caused by different event types. Exact equations are derived, as well as asymptotic expressions which facilitate the application of the method. Due to sampling variability, the estimate of lifetime load effect varies for each sample of load effect taken. In this work, the method of predictive likelihood is used to calculate the variability of the predicted extreme for a given sample. In this manner, sources of uncertainty can be taken into account and the resulting lifetime load effect is shown to be calculated with reasonable assurance. To calculate the total lifetime load effect static load effect plus that due to dynamic interaction), the results of dynamic simulations based on 10-years of static results are used in a multivariate extreme value analysis. This form of analysis allows for the inherent correlation between the total and static load effect that results from loading events. A distribution of dynamic amplification factor and estimates for a site dynamic allowance factor are made using parametric bootstrapping techniques. It is shown that the influence of dynamic interaction decreases with increasing static load effect

The impact of human errors on the performance to failure of concrete bridges

Programa doutoral em Engenharia CivilO colapso de pontes que tiveram lugar em todo o mundo nos últimos 50 anos destacou o erro humano como a principal causa do colapso de pontes. Dadas as implicações financeiras, sociais e psicológicas de tais eventos indesejados, a contribuição do erro humano no colapso de pontes deve ser investigada com o objetivo de compreender como é que a robustez e a segurança estrutural das pontes são afetadas pelos mesmos. A deterioração das pontes, leva à redução das margens de segurança, expondo muitas vezes deficiências causadas por erros de projeto e construção, realçando a importância do desenvolvimento de procedimentos de avaliação estrutural mais abrangentes, tendo em conta numerosas fontes de incertezas. Apesar destes factos conhecidos existem poucos trabalhos disponíveis investigando questões tão relevantes. Neste sentido este trabalho aborda a identificação dos erros humanos em suas inúmeras formas, ou seja, erros de projeto e erros de construção, de acordo com opiniões de especialistas e eventos de colapso de pontes registados. Diferentes erros representam diferentes ameaças à segurança estrutural; como tal o risco relativo dos erros também é investigado. O real impacto dos erros humanos na segurança estrutural é investigado através de três pontes de betão armado, considerando a probabilidade de falha perante um conjunto de incertezas como principal indicador de desempenho. Tal investigação é realizada em duas etapas, uma onde os erros de projeto e construção são introduzidos em cenários onde se entende que eles estão presentes e outra onde a possibilidade de ocorrência de erros de construção é investigada considerando a probabilidade do erro humano e a magnitude do erro. Ocorrências únicas e múltiplas de erros também são discutidas. Modelos de elementos finitos, considerada para fins de análise estrutural não linear, e modelos substitutos são introduzidos como a base das múltiplas análises de fiabilidade estrutural realizadas. Finalmente, a previsão da vida útil de pontes considerando a corrosão induzida por carbonatação e a redução da vida útil das pontes causada por erros de construção são questões também abordadas.The collapse of bridges that have taken place worldwide in the last 50 years has highlighted human error as the main cause of the collapse of bridges. Given the financial, social and phycological implications of such hazardous events, human errors' contribution to the collapse of bridges must be investigated, aiming to understand how their robustness and structural safety are affected. The ageing of bridges leads to safety margin reductions that often expose deficiencies caused by design and construction errors, underling the importance of developing more comprehensive frameworks that consider numerous sources of uncertainty for structural safety assessment purposes. Despite these facts and known needs, few works facing such relevant concerns are available. Accordingly, human errors are identified in their numerous forms, i.e., design errors and construction errors, according to expert opinions and real-world bridge collapse events. Different errors represent different threats to structural safety; thus, their relative risk is also investigated. The actual impact of human errors on structural safety is investigated through one reinforced and two prestressed concrete bridges, using their probability of failure, given a group of uncertainties, as the main performance indicator. Such investigation is performed on two fronts, one where design and construction errors are introduced under scenarios where they are understood to be present, and another where the possibility of occurrence of construction errors is investigated considering probabilistic models to describe human error probabilities and error magnitudes. Single and multiple occurrences of errors are also discussed. Finite element modelling, considered for non-linear structural analysis purposes, and surrogate models are introduced as the backbone of the multiple structural reliability analysis performed. Finally, the service life prediction of bridges considering carbonation-induced corrosion and the service life reduction of bridges due to construction errors are carefully addressed.This work was partially financed by (i) national funds through FCT - Foundation for Science and Technology, under grant agreement “PD/ BD/143003/2018” attributed to the PhD Candidate through the iRail Doctoral program; and (ii) FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020