Reliability based live loads for structural assessment of bridges on heavy-haul railway lines

The highest live loads on railway lines are on dedicated freight corridors operated as heavy-haul lines. These lines carry high axle loads above 25 tonnes and total tonnage above 20 million tonnes per annum over distances greater than 150km. The South African iron ore line currently operates long trains of length 4.1km with 30 tonne per axle wagons on a narrow gage (1065mm) line over a distance of 861km. The operation of heavy haul lines require close monitoring and structural performance evaluation of existing bridges. This study covered both analytical studies and field measurements of bridge dynamic response and static vertical loads required to compute moments shear for beam-type bridges. The field study of dynamic amplification factors was based on strain measurements on the Olifants bridge located on the heavy-haul iron line in South Africa. The Olifants bridge is a 23 span box girder consisting of 2 continuous span segments of 11 spans at either end and a drop span in the middle. The collected strain data consisted 1174 loaded and 1372 empty train crossing events from June 2016 to March 2017. The probabilistic study was based on weigh-in-motion data of heavy-haul freight collected from January 2016 to August 2016. The study was limited to single span, 2 span and 4 span bridges with equal spans and did not consider fatigue. The dynamic response parameters of interest were frequency time evolution of bridge under heavy loads and dynamic amplification factors. An approximate formula derived using 2 dimensional beam model with moving masses is presented. The approximate formulae predicts the reduced frequency within 12% of the estimate from field vibration measurements of an 11 span continuous bridge with train to bridge linear mass ratio of 88%. The approximate formula underestimates the frequency as the stiffening contribution from train suspension system is ignored in a moving mass approximation. Dynamic amplification factors from strain measurements of a continuous 11 span bridge where considerably higher with maximum of 12% compared to 5% from a moving force analytical model for train speed below 60km/h. The amplification from measurements were considerably higher due to the additional local amplification of strains in upper flange of the box girder. A comparison of amplification factors for loaded and empty trains shows that increase in gross weight increases amplification factors. Furthermore, dynamic amplification factors are not dependent on changes in speed during train crossing. Different extrapolation techniques were used to obtain load effects from the same block maxima data. It was shown that the normal, GEV and Bayesian extrapolation methods give load effects within 1% of each other with the normal extrapolation being marginally on the lower end. This observation holds across beam types and span lengths from 5m to 50m. Although the GEV allows for all the three extreme type distributions, an analysis based on available weigh-in-motion data of axle weights show that the fitted distributions using Bayesian and Maximum Likelihood Estimate for all load effects for the span ranges are all Weibull type. On the other hand it is known that the domain of attraction for the normal distribution is Gumbel type. The study also found that extrapolated loads effects are less sensitive to increase in return period beyond 50 years. This aspect is significant as return period is a measure of safety target when determining design values for loads. The study investigated the impact of traffic volume increase and wagon axle load dependencies. The load effects on heavy-haul were shown to be more sensitive to the weak dependence than to traffic growth over the remaining service life of 50 years. The increase in return levels of load effects is less than 1% for traffic volume growth of 4% over a period of 50 years in contrast to the much higher values between 6% and 9% reported on highway bridges for 3% traffic volume growth over 40 year period. Assessment loads that account for some wagon axle dependence have lower return values of load effects than the assume that axle loads are independent which is consistent with theory

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

Novel Approaches for Structural Health Monitoring

The thirty-plus years of progress in the field of structural health monitoring (SHM) have left a paramount impact on our everyday lives. Be it for the monitoring of fixed- and rotary-wing aircrafts, for the preservation of the cultural and architectural heritage, or for the predictive maintenance of long-span bridges or wind farms, SHM has shaped the framework of many engineering fields. Given the current state of quantitative and principled methodologies, it is nowadays possible to rapidly and consistently evaluate the structural safety of industrial machines, modern concrete buildings, historical masonry complexes, etc., to test their capability and to serve their intended purpose. However, old unsolved problematics as well as new challenges exist. Furthermore, unprecedented conditions, such as stricter safety requirements and ageing civil infrastructure, pose new challenges for confrontation. Therefore, this Special Issue gathers the main contributions of academics and practitioners in civil, aerospace, and mechanical engineering to provide a common ground for structural health monitoring in dealing with old and new aspects of this ever-growing research field

Studies of Sensor Data Interpretation for Asset Management of the Built Environment

Sensing in the built environment has the potential to reduce asset management expenditure and contribute to extending useful service life. In the built environment, measurements are usually performed indirectly; effects are measured remote from their causes. Modelling approximations from many sources, such as boundary conditions, geometrical simplifications and numerical assumptions result in important systematic uncertainties that modify correlation values between measurement points. In addition, conservative behavior models that were employed - justifiably during the design stage, prior to construction - are generally inadequate when explaining measurements of real behavior. This paper summarizes the special context of sensor data interpretation for asset management in the built environment. Nearly twenty years of research results from several doctoral thesis and fourteen full-scale case studies in four countries are summarized. Originally inspired from research into model based diagnosis, work on multiple model identification evolved into a methodology for probabilistic model falsification. Throughout the research, parallel studies developed strategies for measurement system design. Recent comparisons with Bayesian model updating have shown that while traditional applications Bayesian methods are precise and accurate when all is known, they are not robust in the presence of approximate models. Finally, details of the full-scale case studies that have been used to develop model falsification are briefly described. The model-falsification strategy for data interpretation provides engineers with an easy-to-understand tool that is compatible with the context of the built environment

Hybrid structural health monitoring using data-driven modal analysis and model-based Bayesian inference.

Civil infrastructures that are valuable assets for the public and owners must be adequately and periodically maintained to guarantee safety, continuous service, and avoid economic losses. Vibration-based structural health monitoring (VBSHM) has been a significant tool to assess the structural performance of civil infrastructures over the last decades. Challenges in VBSHM exist in two aspects: operational modal analysis (OMA) and Finite element model updating (FEMU). The former aims to extract natural frequency, damping ratio, and mode shapes using vibrational data under normal operation; the latter focuses on minimizing the discrepancies between measurements and model prediction. The main impediments to real-world application of VBSHM include 1) uncertainties are inevitably involved due to measurement noise and modeling error; 2) computational burden in analyzing massive data and high-fidelity model; 3) updating structural coupled parameters, e.g., mass and stiffness. Bayesian model updating approach (BMUA) is an advanced FEMU technique to update structural parameters using modal data and account for underlying uncertainties. However, traditional BMUA generally assumes mass is precisely known and only updating stiffness to circumvent the coupling effect of mass and stiffness. Simultaneously updating mass and stiffness is necessary to fully understand the structural integrity, especially when the mass has a relatively large variation. To tackle these challenges, this dissertation proposed a hybrid framework using data-driven and model-based approaches in two sequential phases: automated OMA and a BMUA with added mass/stiffness. Automated stochastic subspace identification (SSI) and Bayesian modal identification are firstly developed to acquire modal properties. Following by a novel BMUA, new eigen-equations based on two sets of modal data from the original and modified system with added mass or stiffness are derived to address the coupling effect of structural parameters, e.g., mass and stiffness. To avoid multi-dimensional integrals, an asymptotic optimization method and Differential Evolutionary Adaptive Metropolis (DREAM) sampling algorithm are employed for Bayesian inference. To alleviate computational burden, variance-based global sensitivity analysis to reduce model dimensionality and Kriging model to substitute time-consuming FEM are integrated into BMUA. The proposed VBSHM are verified and illustrated using numerical, laboratory and field test data, achieving following goals: 1) properly treating parameter uncertainties; 2) substantially reducing the computational cost; 3) simultaneously updating structural parameters with addressing the coupling effect; 4) performing the probabilistic damage identification at an accurate level

Symmetry in Structural Health Monitoring

In this Special Issue on symmetry, we mainly discuss the application of symmetry in various structural health monitoring. For example, considering the health monitoring of a known structure, by obtaining the static or dynamic response of the structure, using different signal processing methods, including some advanced filtering methods, to remove the influence of environmental noise, and extract structural feature parameters to determine the safety of the structure. These damage diagnosis methods can also be effectively applied to various types of infrastructure and mechanical equipment. For this reason, the vibration control of various structures and the knowledge of random structure dynamics should be considered, which will promote the rapid development of the structural health monitoring. Among them, signal extraction and evaluation methods are also worthy of study. The improvement of signal acquisition instruments and acquisition methods improves the accuracy of data. A good evaluation method will help to correctly understand the performance with different types of infrastructure and mechanical equipment

Damage and material identification using inverse analysis

In this thesis, we formulate novel solutions to two inverse problems using optical measurements as input data: i) local level damage identification of beams, and ii) material constitutive parameter identification using digital image correlation measurement of surface strain/displacements. A novel photogrammetric procedure based on edge-detection was devised to measure the quasi-continuous deflection of beams under given loading. This method is based on the close-range photogrammetry technique made possible through recent developments of image processing algorithms and modern digital cameras. Two computational procedures to reconstruct the stiffness distribution and to detect damage in Euler-Bernoulli beams are developed in this thesis. The first formulation is based on the principle of the equilibrium gap along with a finite element discretization. The solution is obtained by minimizing a regularized functional using a Tikhonov Total Variation (TTV) scheme. The second proposed formulation is a minimization of a data discrepancy functional between measured and model-based deflections. The optimal solution is obtained using a gradient-based minimization algorithm and the adjoint method to calculate the Jacobian. The proposed identification methodology is validated using experimental data. The proposed methodology has the potential to be used for long term health monitoring and damage assessment of civil engineering structures. The identification of material plasticity parameters is carried out by minimizing a least-square functional measuring the gap between inhomogeneous displacement fields obtained from measurements and finite element simulations. The material parameters are identified simultaneously by means of direct, derivative-free optimization methods where the finite element simulation is treated as a black-box procedure. Methods verifying and validating the identified results are given. Particular interest is given to the identifiability issue in deterministic and statistical sense. The validation procedure intends to detect false positive results (type-II errors). The performance of the computational procedures is illustrated by numerical and experimental examples. The proposed approach avoids using the gradient of the cost function in the identification process; it has the benefit of allowing the use of any finite element code as a black box to solve the direct problem

Bayesian networks for the multi-risk assessment of road infrastructure

The purpose of this study is to develop a methodological framework for the multi-risk assessment of road infrastructure systems. Since the network performance is directly linked to the functional states of its physical elements, most efforts are devoted to the derivation of fragility functions for bridges exposed to potential earthquake, flood and ground failure events. Thus, a harmonization effort is required in order to reconcile fragility models and damage scales from different hazard types. The proposed framework starts with the inventory of the various hazard-specific damaging mechanisms or failure modes that may affect each bridge component (e.g. piers, deck, bearings). Component fragility curves are then derived for each of these component failure modes, while corresponding functional consequences are proposed in a component-level damage-functionality matrix, thanks to an expert-based survey. Functionality-consistent failure modes at the bridge level are then assembled for specific configurations of component damage states. Finally, the development of a Bayesian Network approach enables the robust and efficient derivation of system fragility functions that (i) directly provide probabilities of reaching functionality losses and (ii) account for multiple types of hazard loadings and multi-risk interactions. At the network scale, a fully probabilistic approach is adopted in order to integrate multi-risk interactions at both hazard and fragility levels. A temporal dimension is integrated to account for joint independent hazard events, while the hazard-harmonized fragility models are able to capture cascading failures. The quantification of extreme events cannot be achieved by conventional sampling methods, and therefore the inference ability of Bayesian Networks is investigated as an alternative. Elaborate Bayesian Network formulations based on the identification of link sets are benchmarked, thus demonstrating the current computational difficulties to treat large and complex systems