Performance Assessment Of Bridges Research Articles

Enhanced Multi‐Objective Optimization Model for Bridge Performance Assessment and Prediction, Based on Improved PCA, K‐Means Clustering, and Kaplan–Meier Survival Algorithm

ABSTRACTThe research proposes a hybrid algorithm model that combines model‐driven and data‐driven approaches for the direct application of bridge health monitoring technology in bridge management. This comprehensive study encompasses a series of analytical techniques and methodologies to build a multi‐objective optimization model for bridge performance assessment and prediction. It focuses on the processing of multi‐source heterogeneous data, selection of key sub‐parameters using Principal Component Analysis (PCA), enhanced K‐means clustering analysis, determination of structural component target thresholds, time‐dependent survival probability analysis, regression fitting, and timing prediction of the bridge system for both the components of double‐layer truss arch bridge and the bridge system. The initial phase of the study concentrates on the diversification and decentralization of monitored data from various sources, integrating and cleaning data obtained from different sources to ensure data quality and consistency. PCA technique is applied to identify key sub‐parameters that have significant impacts on the performance of structural components. Enhanced K‐means clustering analysis is carried out to effectively group and classify the identified key sub‐parameters. Numerical simulations, including structural nonlinear analysis, are conducted to determine the target thresholds of bridge structure, providing important benchmarks for performance evaluation. Finally, a multi‐parameter regression model is used to evaluate and update the performance of the bridge structure, taking into account survival probability (using the Kaplan–Meier method), maintenance history, and material deterioration to estimate the most critical time for the bridge system. A case study is conducted to validate the suggested comprehensive algorithms for a double‐layer truss arch combination bridge, which contributes to enhancing performance evaluation and predicting the most critical time for structural components and bridge system in the bridge management and maintenance practices. It should not be ignored that, the accuracy and reasonability of bridge structure system performance evaluation and prediction depend largely on the selection of target thresholds.

Online Bridge Structural Condition Assessment Based on the Gaussian Process: A Representative Data Selection and Performance Warning Strategy

Data-driven methods have now been widely used in structural health monitoring of civil infrastructures thanks to the rapid development of sensor technologies with massive structural and operational condition data. One main issue of data-driven methods is that the computational time increases with the number of monitoring data used, which limits their applications for online structural condition assessment. Focusing on bridge structural health monitoring, this paper proposes a representative data selection strategy for online performance assessment based on Gaussian process models. The proposed method can effectively reduce the required monitoring data size for training, allowing the bridge performance assessment to be conducted in a real-time manner. The method is developed in a probabilistic manner, allowing associated uncertainty of bridge monitoring data to be rigorously considered. A probabilistic warning index is proposed for bridge condition assessment and anomaly detection. The proposed method is validated using synthetic data and applied to structural condition assessment of two full-scale bridges, illustrating the feasibility for real implementations.

High dimensional model representation for flood fragility analysis of highway bridge

Climate changes and global warming have increased the frequency and severity of rains, hence the vulnerability of structures due to floods. Bridges are the most affected components of the transportation infrastructure affected by floods. Analysis in a probabilistic framework is required for reliable performance assessments of bridges. However, the detailed performance assessment of bridges requires consideration of the uncertainties in geometry, material properties, and flood loading. The random structural responses due to flood loading can be obtained by stochastic analysis; conventionally, this is carried out using Monte Carlo Simulations (MCS). Due to the high computational cost required for MCS, non-statistical methods like High Dimensional Model Representation (HDMR) can be a feasible alternative to MCS. The present study is an application of the HDMR method to develop metamodels for the flood response of the highway bridge, considering the uncertainties. The structural responses due to flood at the sampling points of the HDMR are obtained by conducting finite element analysis. The HDMR could predict the random bridge responses due to flood loading fairly well compared to other popular response surface methods (like Central Composite Design, Box Behnken Design, and Full Factorial Design) with significantly fewer simulations. HDMR provides a flood response metamodel, as a function of the sensitive random parameters, with a substantial reduction in the computational effort. The efficiency of the HDMR method can be attributed to the ability of HDMR to model the nonlinear flood responses in terms of the statistical properties of random variables. The metamodels for the pier drifts developed are further utilized to generate flood fragility curves. The HDMR metamodels are found to simplify the fragility computations.

In-situ test and model updating of an RC tied-arch bridge

The Italian bridge asset is dated, and the functionality of many bridges is jeopardised by degradation due to ageing and environmental effects, as well as by increasing traffic loads. An adequate operational monitoring and maintenance program is essential for ensuring the safety condition of these critical structures. Administrations in charge and policymakers must clearly understand the actual condition of the infrastructure asset in order to optimise maintenance and retrofit planning. For this purpose, visual inspections might fail to reflect the current condition accurately; thus, additional experimental investigation campaigns are desirable. In the knowledge process for existing bridges, valuable information can be obtained through appropriate experimental tests, such as material testing and dynamic identification. Moreover, the potential of on-site test results in updating structural modelling has been demonstrated, successfully improving the performance assessment of bridges. However, sensitivity-based Finite Element model updating requires a deep knowledge of the examined structure to determine which parameters mainly affect the dynamic response.In this framework, the contribution of this paper consists of a typological analysis and model updating of an existing RC tied-arch bridge dating back to the 1930s. The structural condition was assessed by an on-site investigation campaign, including a geometric survey, material testing, and ambient vibration test (AVT). For the latter, three algorithms for operational modal analysis were performed to extract the bridge's modal parameters and then compare outcomes. Lastly, results from experimental tests were used to update a 3D finite element model.

Polya urn model-based performance assessment of PSC bridges: prestress loss consideration

A methodology for performance assessment of prestressed concrete (PSC) girder bridge system, based on strain monitoring in limited number of girders, is proposed in this paper. The methodology uses Polya urn model for determining probabilities of the bridge system being in different condition states with respect to loss of prestress. Performability measure is used for describing the performance of the bridge system. A condition state is assigned for the bridge system from a predefined set of condition states. The time for detailed inspection is determined as the time instant at which the performability of the bridge system becomes less than the target/required performance level. Performance assessment of a bridge system with one hundred PSC girders is considered for illustrating the methodology. The obtained values of condition state probabilities and performability for the considered scenarios (i.e., different number of monitored girders with prestress loss exceeding the allowable value) suggest that the methodology is able to consider the value of available information.

Long‐term bridge performance assessment using clustering and Bayesian linear regression for vehicle load and strain mapping model

The weigh-in-motion (WIM) system and the structural health monitoring (SHM) system have been used as two separate modules playing different roles in bridge operation and providing different information for bridge maintenance. This study proposes a novel bridge safety condition assessment method that utilizes long-term monitoring data from the WIM system and the SHM system. The method uses the slope of the established vehicle load and vehicle-induced strain mapping model as the evaluation indicator for bridge condition assessment and early warning by clustering and Bayesian linear regression. The proposed method is verified with the continuous monitoring data of a concrete box girder bridge. The results show that the slope indicator of the mapping model changes with the variation of bridge performance, which is stable and can reflect the bridge state in time. The evaluation method can integrate the WIM system with the SHM system and evaluate the bridge health condition based on the correspondence between the two systems, which can make full use of the data.

Reliability Assessment Method for Simply Supported Bridge Based on Structural Health Monitoring of Frequency with Temperature and Humidity Effect Eliminated

Structural health monitoring (SHM) has been widely used for the performance assessment of bridges, especially the methods based on dynamic characteristics. Meanwhile, bridge modal frequency is influenced significantly by environmental factors, such as temperature and humidity. Combined with SHM, a reliability assessment of bridges with the temperature and humidity effects eliminated is proposed. Firstly, the bridge deflection verification coefficient is adopted as the evaluation indicator for bridge condition, which is the ratio of deflection-measured value to deflection-calculated value. It is obtained from the relationship between verification coefficient and modal frequency through theoretical derivation. Secondly, a back propagation (BP) neural network is improved by using an artificial bee colony algorithm and employed as a surrogate model to eliminate the effect of temperature and humidity on frequency. Thirdly, a dynamic Bayesian network is applied to establish the reliability analysis model combined with the monitoring results, so that the probability distribution of bridge parameters is updated to improve the accuracy of the reliability analysis. Finally, a simply supported bridge is used as the case study, based on the proposed method in this work. The results indicate that the proposed method can eliminate the temperature and humidity effect on frequency precisely and effectively. With the effect of temperature and humidity on frequency eliminated, the bridge condition assessment can be evaluated accurately through the reliability analysis based on SHM and the dynamic Bayesian network.

Reconstruction as Deterioration Indicator for Operational Structural Performances of Bridge Materials

This article presents analysis of deterioration indicators for a comparative quantification of the operational structural performances of bridge inventories. The analysis considers the various kinds of material in the entire database of the US National Bridge Inventory. The multi-criteria approach integrates the indicators of deterioration, considering diagnostic condition and life cycle to determine the equivalent operational structural performances. This study also uses reconstruction data to determine an additional deterioration indicator for performance. The proportional effort for reconstruction reflects the practical need to counter deterioration and preserve a required level of structural performance, under all circumstances. The presented addition of reconstruction performance provides a more comprehensive assessment of bridge performance. The results reveal the continuous efforts to maintain a relatively balanced performance adequacy at the national level within the prevailing funding constraints. Reconstruction performances are generally higher than deterioration trends (rate and pattern) performances, revealing that the constrained reconstruction efforts have difficulty to keep pace with the actual deterioration.

Digital twin-based collapse fragility assessment of a long-span cable-stayed bridge under strong earthquakes

Fragility analysis is widely used in seismic performance assessment of bridges but uncertainties in seismic demand and bridge modelling affect the accuracy of assessment results. This study proposes a digital twin-based collapse fragility assessment method for long-span cable-stayed bridges under strong earthquakes. A scaled long-span cable-stayed bridge and its shake table tests are taken as an example. Three finite element (FE) models of the bridge, including a design document-based FE model, a linearly updated FE model, and a nonlinearly updated FE model, are established to demonstrate the necessity of the digital twin-based assessment. Incremental dynamic analyses (IDA) are conducted to calculate the collapse fragility curves of the FE models. The assessment results are compared with the test results in terms of collapse mechanisms, collapse ground motion intensities, and collapse probabilities. It is found that the proposed method is feasible and accurate for seismic collapse assessment of long-span cable-stayed bridges.

Mainshock-Integrated Aftershock Vulnerability Assessment of Bridge Structures

Seismic fragility analysis is often conducted to quantify the vulnerability of civil structures under earthquake excitation. In recent years, besides mainshocks, strong aftershocks have been often witnessed to induce structural damage to engineered structures, including bridges. How to accurately and straightforwardly quantify the vulnerability of bridges due to sequential mainshocks and aftershocks is essential for an efficient assessment of bridge performance. While recognizing the limitation of existing methods, this paper proposes a mainshock integrated aftershock fragility function model, which empirically encodes the effects of mainshocks and retains the simple form of traditional fragility curves. A pile foundation-supported bridge system is modeled considering seismic soil-structure interaction to demonstrate the proposed fragility model. Numerical examples show that the resulting fragility curves incorporate the initial value for the probability of collapse of the bridge system due to a mainshock and the effects of the variable aftershocks conditional on the mainshock. Statistical analysis confirms that the proposed model fits the simulated vulnerability data (e.g., seismic intensities of aftershocks and the response demands conditional a select mainshock ground motion) both accurately and robustly.

Displacement shape measurement of continuous beam bridges based on long-gauge fiber optic sensing

Abstract Displacement shape is an important index for performance assessment of bridges. It is usually hard to find “reference point” for direct arrangement of displacement transducers on real bridges, and therefore indirect measurement methods using strain sensors attracts increasing attention. A reference-free method for monitoring displacement shape of continuous beam bridges using long-gauge fiber optic sensors is proposed in this article. Firstly, the method for calculating the displacement shape of continuous beam bridges using fiber optic strain sensors along the whole structure was theoretically established. Meanwhile, displacement shape determination of a certain span using fiber optic strain sensors along the corresponding span was also presented for the purpose of cost saving in practical engineering. The presented method is independent of external loads and could be applied to both static and dynamic loads. Results from numerical simulation of a three-span bridge show that both methods could obtain the displacement shape of each span with high precision. Furthermore, experimental results indicate that the presented method could perform online monitoring of time-displacement series at each cross section with high accuracy, and could achieve the displacement shape along the continuous beam bridge at a certain time. Therefore, it could be a convenient reference-free strategy for monitoring the displacement shape of continuous beam bridges in actual engineering.

Analysis of thermal deformations for a long-span arch bridge using long-term monitoring data

Deformation is an important parameter indicating bridge integrity and serviceability. Fully understanding deformation induced by temperature variations plays an indispensable role in developing deformation-based indexes for bridge performance assessment as well as predicting the displacement amplitude under normal operation environment. In this paper, long-term monitoring data are used to facilitate investigating the thermal deformation properties for a long-span arch bridge. The time-varying features of thermal deformation in longitudinal, vertical, and transversal directions are first discussed. Then, the relationship among thermal deformation in the three directions is extracted. Following that, the thermal correlation of deformation is explored. The results show that the longitudinal and vertical deformation depends heavily on seasons and the thermal deformation in the three directions has close relationships. The vertical displacement has nonlinear correlation with the temperature. The results are expected to provide reference values for similar types of arch bridges.

Analysis of thermal deformations for a long-span arch bridge using long-term monitoring data

Deformation is an important parameter indicating bridge integrity and serviceability. Fully understanding deformation induced by temperature variations plays an indispensable role in developing deformation-based indexes for bridge performance assessment as well as predicting the displacement amplitude under normal operation environment. In this paper, long-term monitoring data are used to facilitate investigating the thermal deformation properties for a long-span arch bridge. The time-varying features of thermal deformation in longitudinal, vertical, and transversal directions are first discussed. Then, the relationship among thermal deformation in the three directions is extracted. Following that, the thermal correlation of deformation is explored. The results show that the longitudinal and vertical deformation depends heavily on seasons and the thermal deformation in the three directions has close relationships. The vertical displacement has nonlinear correlation with the temperature. The results are expected to provide reference values for similar types of arch bridges.

Reliability Importance as a Measure of Bridge Element Condition Index for Deteriorating Bridges

As a concerted effort to improve the outcomes of bridge asset management, state highway agencies and Departments of Transportation (DOT) often improve their modes of bridge performance assessment using advanced deterioration models, optimized cost models, and other multi-objective preservation analysis in bridge monitoring processes. This paper attempts to utilize reliability importance index of a bridge component as a performance measure of the criticality of each element or subsystem to the entire bridge system. This paper investigates three major concepts regarding the importance of bridge elements: (i) the contribution of individual bridge elements to the overall reliability of the bridge system, (ii) the effect of critical elements on bridge deterioration, and (iii) the rank of the importance of elements and the potential economic consequence of their failure for optimal resource allocation. Computational methodologies are applied to legacy inspection data for bridges monitored over 20 years to evaluate the probability of failure of certain bridge elements. Based on the reliability importance criterion, the superstructure and substructure elements were considered to be critical to the survival of bridges. Bridge management and miscellaneous elements (such as expansion joints, railings, and sign structures), though important, did not generally contribute to the overall deterioration of the bridge.

Implications of bidirectional interaction on nonlinear seismic response of steel piers

The seismic performance assessment of bridges by nonlinear time history analysis using unidirectional spectrum-matched accelerograms is generally adopted in Japan's specifications. However, the simultaneous action of two horizontal components is known to induce a potentially higher inelastic response in steel piers relative to that under unidirectional excitation. In this background, a rising concern within research communities is the bidirectional effect on the risk of bridge failure or damage under a given seismic intensity during the service life of a bridge. In this study, the difference in the risk of steel pier damage/failure between unidirectional ground motion and bidirectional ground motion is assessed using seismic fragility. Fragility curves are constructed by subjecting a single steel pier with various geometric designs to a large number of earthquake ground motions using the incremental dynamic analysis methodology. The study indicates that the damage state of collapse is not changed by bidirectional excitations, whereas the use of bidirectional excitations in less damaging states reduces the bridge seismic performance and induces higher fragility. The response difference is quantified by introducing an equifragility factor. This factor is calculated as the ratio of intensity of the unidirectional input to the bidirectional input, which causes the same fragility caused by the unidirectional counterpart. The constant characteristic value of the equifragility factor provides useful information for considering the bidirectional effect in seismic design.

Estimating the resistance of aging service-proven bridges with a Gamma process-based deterioration model

Abstract The environmental or anthropogenic factors, to which the in-service bridges are subjected, are responsible for the reduction of bridge performance, and finally lead to great service risk for bridges and increase the probability of substantial economic losses. Probability-based estimate of bridge resistance is an essential indicator for the bridge condition evaluation and for optimization of bridge maintenance/repair decisions. It places an emphasis on the proper probabilistic models of structural properties and assessment methods. Making full use of historical service load information may improve the accuracy of bridge performance assessment with reduced epistemic uncertainty for existing aging bridges. In to-date analyses to update the bridge resistance with past service information, the models of resistance deterioration have been assumed as either deterministic or fully correlated, which may differ significantly from the realistic case. With this regard, this paper proposes a novel method for updating the resistance of service-proven bridges with a realistic deterioration model. The Gamma stochastic process has been suggested in the literature to describe the probabilistic behavior of structural time-dependent resistance and thus is adopted in this paper. An illustrative bridge is presented to demonstrate the applicability of the proposed method. Parametric examples are conducted to investigate the role of resistance deterioration model in the updated estimate of bridge resistance with historical service information.

Three‐dimensional multiscale finite element models for in‐service performance assessment of bridges

AbstractAccurate representation of the structural performance of civil engineering structures, specifically complex bridge structures, may be achieved through an efficient multiscale finite element (FE) model. Multiscale FE modeling couples multiple dimensions of elements in a single model. In this study, the selected existing multipoint constraint equations applied in planar coupling conditions are modified and refined for out‐of‐plane coupling conditions in a single three‐dimensional FE model. Also, the optimum location for the interface points of different elements is determined to improve the model's accuracy and efficiency. The present case study, the Memorial Bridge in Portsmouth, NH, is a vertical lift bridge, which includes novel gusset‐less connections. These connections have complex geometries and therefore require finer dimension elements to represent the structural behavior, while the remainder of the structure is modeled with coarser dimension elements. To achieve an accurate and efficient multiscale model of the Memorial Bridge, multiple global FE models are developed and the predicted structural responses are verified with respect to the field‐collected structural responses of the bridge.

New Dynamic Prediction Approach for the Reliability Indexes of Bridge Members Based on SHM Data

AbstractThe last several decades have witnessed a bridge performance assessment shift from deterministic methodology to probabilistic methodology. Structural health monitoring (SHM) has been subjec...

Structural Health Monitoring for Performance Assessment of Bridges under Flooding and Seismic Actions

Bridges can be subjected to damaging environmental actions due to flooding and seismic hazards. Flood actions that result in scour are a leading cause of bridge failure, while seismic actions that induce lateral forces may lead to high ductility demand that exceeds pier capacity. When combined, seismic actions and scour can lead to effects that depend on the governing scour condition affecting a bridge. Loss of stiffness under scour can reduce the ductility capacity of a bridge but can also lead to an increase in flexibility that may reduce seismic inertial forces. Conversely, increased flexibility can lead to deck collapse due to support loss, so there exists some uncertainty about the combined effect of both phenomena. A necessary step towards the performance assessment of bridges under flooding and seismic actions is to calibrate numerical models that can reproduce structural responses under different actions. A further step is verifying the achievement of performance goals defined by codes. Structural health monitoring (SHM) techniques allow the computation of performance parameters that are useful for calibrating numerical models and performing direct checks of performance goal compliance. In this paper, various strategies employed to monitor bridge health against scour and seismic actions are discussed, with a particular focus on vibration-based damage identification methods.

Identification of structural stiffness and excitation forces in time domain using noncontact vision-based displacement measurement

The emerging noncontact vision-based displacement sensor system offers a promising alternative to the conventional sensors for quantitative structural integrity assessment. Significant advantages of the noncontact vision-based sensor include its low cost, ease of operation, and flexibility to extract structural displacement responses at multiple points. This study aims to link the measured displacement data to the quantification of the structural health condition, by validating the feasibility of simultaneous identification of structural stiffness and unknown excitation forces in time domain using output-only vision-based displacement measurement. Numerical analysis are first carried out to investigate the accuracy, convergence and robustness of identified results to different noise levels, sensor numbers, and initial estimates of structural parameters. Then, experiment on a laboratory scaled beam structure is conducted. Results show that the global stiffness of the beam specimen as well as external hammer excitation forces can be successfully and accurately identified from displacement measurement at two points using one camera. The proposed output-only time-domain identification procedure utilizing vision-based displacement measurement represents a low-cost method for either periodic or long-term bridge performance assessment.