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A smart concrete girder bridge usually has various sensors, based on which several physical properties can be measured, and hence, the health condition can be evaluated. Cracks are always observed on a smart concrete girder bridge. In particular, some of the cracks are induced by overloaded vehicles, which is dangerous to its safe operation. However, due to the crack opening and closing effect, it exhibits nonlinear responses, posing challenges for accurately assessing its health condition. Influence lines (ILs) are a promising indicator for bridge damage. However, there is limited research on the effect of cracks on the ILs of a smart concrete girder bridge. A digital twin is commonly used to accompany the smart sensing system to accurately evaluate the health condition, where the finite element (FE) model is of great importance. Therefore, this study proposes an element size–independent FE model construction method based on the concrete damage plasticity (CDP) model to investigate the changes of displacement and strain ILs of different types of smart concrete girder bridges with bending cracks, which is helpful to guide how to use the ILs to identify the cracks and evaluate the health condition. Initially, a concrete constitutive model based on crushing/fracture energy is proposed, and the evolution law of tensile damage based on fracture energy is derived to construct the element size–independent FE model. Subsequently, experiments on a reinforced concrete (RC) simply supported beam and a prestressed concrete (PC) simply supported bridge subjected to bending failure are used to verify the FE models constructed by the proposed method. Finally, the FE models of a smart RC T‐beam bridge and a smart three‐span PC continuous bridge are established to study the changes in ILs caused by bending cracks. The change of displacement IL at the midspan due to cracks for the smart RC bridge exceeds 10% when the reinforcements yield, while it is less than 10% for the smart PC bridge even if the bridge is in the failure state. The change of both displacement and strain ILs becomes greater when the measurement point approaches the cracks, and the change of strain IL is only detectable when the measurement is close to the cracks. Due to the crack opening and closing effect, the displacement and strain ILs of a smart concrete girder bridge with bending cracks are inconsistent when different loads are applied. The findings can also be used as a pre‐IL‐based crack detection using the passing inspection vehicle‐induced dynamic response on a selection of type of ILs, determination of layout of sensors, and mass of inspection vehicle. [ABSTRACT FROM AUTHOR]

Many damage identification methods are based on a comparison of structure response between healthy and damaged states. However, the healthy state response of especially old bridges is rarely available. In this paper, a new output-only damage identification approach is proposed to locate damage in multi-span bridges. In this method, only the damaged state strain and displacement influence lines are required. This approach is theoretically explained for a three-span girder. The middle span, the health condition of which is in quest, is modeled as a single-span beam, supported by rotational springs with unknown stiffness. A new algorithm is proposed to estimate the healthy state displacement influence line of the middle point of the span. In this algorithm, the ratio of the flexural stiffness of the girder to the spring's rotational stiffness is determined in such a way that the estimated displacement, is as close to the measured response, as possible. Then, the estimated displacement is scaled based on two constraints. First, it is expected to be lower than the pseudo-static component of measured displacement because damage increases the response globally. Second, the difference between the scaled and pseudo-static component of the measured response is minimized. Eventually, damage is located using a novel damage index based on the local deviation of pseudo-static component of measured displacement from the estimated scaled response. The proposed method is numerically evaluated in a three-span girder. The damage is successfully identified in all different scenarios. Also, its application to Powder Mill Pond Bridge is numerically investigated which yielded promising results. [ABSTRACT FROM AUTHOR]

Bridge influence lines (BILs) and bridge influence surfaces (BISs) are inherent static parameters of bridges which can be extracted from moving vehicle-induced bridge responses. Compared with dynamic parameters, these parameters are directly related to the stiffness and internal forces in each cross-section of a bridge therefore can be considered as an effective bridge metamodel. To accelerate the engineering practice of BIL- and BIS-based bridge evaluation, this paper first briefly reviews the current BIL and BIS field test and identification methods. Then, the bridge evaluation guidelines of China and the United States are introduced as the basis of the evaluation methods. Engineering application scenarios for bridge evaluation, including permit load verification, performance degradation checking, and load carrying capacity evaluation, are summarized, and a multiple-scenario bridge evaluation method is established. At the end of this paper, an evaluation example of a four-span continuous bridge is presented to illustrate the application procedure and verify the effectiveness of the proposed method. The outcomes of this paper provide a promising application method of field test BILs and BISs, which may help bridge engineers more effectively use these parameters in engineering practice. [ABSTRACT FROM AUTHOR]

Simply supported bridges are the main bridge types in many transportation systems, and their damage can significantly reduce their load-carrying capacity. To detect possible damage, the time history and spatial distribution of the static responses of bridges (deflection, rotation, and strain influence lines/deformation curves) have recently been proposed as important indicators due to their good local damage detection ability and low testing cost. This paper attempts to establish connections between different static curve-based damage indicators through the various forms of Maxwell-Betti's law. Damage indicators related to seven static curves are then rewritten as a unified framework. The framework states that all these static curves are directly related to the flexural stiffness distribution of the main girder for the simply supported bridge. Then, the theoretical formulations for the difference between the static curves of bridges in intact and damaged states are derived, and the response difference surfaces (RDSs) are plotted to visualize the sensitivity of different static curves to damage. Sensor placement suggestions for stiffness degradation evaluation are presented based on the damage sensitivity analysis at the end of this paper. The results of this study provide comprehensive theoretical support for static response-based damage identification of simply supported bridges. [ABSTRACT FROM AUTHOR]

This study looks at the load distribution among beams in a 3-dimensional bridge Finite Element Model under normal traffic loading and the resulting effect of damage, modelled as a localised loss of elements in a beam. The load distribution is captured, both transversely across the girders and also longitudinally along the girders, and used to identify damage that is remote from the strain sensor location. When there is a loss of elements in a section of the bridge (e.g. due to a strike from a vehicle passing underneath), a portion of the load will redistribute along alternative load paths in the structure. This is most pronounced when the position of the loading and the location of the damage align but also happens when they do not. The study examines the effect of vehicles of different weight and axle configuration on the load distribution, and how grouping results by axle configuration can overcome this. A further novelty of this approach is that the strain response can be transformed into an influence line, which then allows data from large populations of general traffic to be used irrespective of axle configuration. The effect of various transverse positions of vehicles within the driving lane is overcome by combining data from large populations of vehicles. An advantage of this method is that the load distribution from a vehicle crossing is calculated as a ratio i.e. when the bridge is experiencing the same general environmental conditions, and thus reducing the difficulty in controlling the effects of varying environmental conditions when comparing results to a healthy baseline. • Monitoring of load redistribution both transversely and longitudinally along girders. • Use of the whole strain signal from a vehicle crossing (i.e. not just maximum strain). • The bridge response to vehicles can be transformed into a strain axle influence line. • Large populations of general traffic can be used. • Small deviations in transverse position of vehicles overcome. [ABSTRACT FROM AUTHOR]

With an increase in bridge service time, damage will inevitably occur. Accurately identifying damage is of great significance to the operation and maintenance of a bridge. To realize a frequent identification of local stiffness reduction caused by structural damage in operation, this paper proposes a new method for damage localization of short-span bridges based on the axle weight time history identified by bridge weigh-in-motion technology. This method is based on a least squares QR decomposition recursive algorithm, and the orthogonal matrix is updated by the newly added measured rotation response and the axle weight identified in the previous step to recursively obtain the current axle weight. Then, the axle weight time history is obtained and the damage is located according to the mutation of axle weight time history caused by the first axle passing through the damage. The accuracy and applicability of the proposed method are verified by a numerical model of a simply supported beam. The results show that the axle weight time history has a strong sensitivity to damage, and the average normalized axle weight time history can accurately locate the damage and significantly reduce the impact of environmental noise. This method can be used for long-term health monitoring of bridge structures. [ABSTRACT FROM AUTHOR]

The influence line is an important static property in bridges. The shape and magnitude of the girder bridge influence line contain stiffness information for the bridge supports and beam. This research sought to directly identify the rotational stiffness of bridge supports and the flexural stiffness of beams from extracted influence lines, which provides a quantification method for bridge damage and performance degradation. The paper first reviews the influence line identification method from vehicle-induced bridge responses. Then, the complete theoretical formulations of the deflection, rotation, and strain influence line for a general girder bridge are derived. Based on these derivations, the rotational stiffness at two supports and the equivalent flexural stiffness value of the beam are identified. Next, a certain span of a continuous girder bridge is converted to an equivalent simply supported system, and the flexural stiffness distribution of the beam is estimated based on the bending moment-curvature relationship and Betti's law. The paper then presents a three-span continuous girder bridge verification example. The stiffness estimation results under different test conditions combined with the bridge influence line identification algorithm are presented to verify the reliability of the proposed method. This stiffness estimation method can be applied to baseline-free bridge performance evaluations based on vehicle-induced bridge responses. [ABSTRACT FROM AUTHOR]

The article presents a technique that significantly reduces the number of calculations when plotting internal forces and influence lines in statically determined multi- span beams. The interaction scheme of the individual beams are not stored, but with the use of the axiom of constraints, it is presented in a more general form- in a straight line, without separation of the main and auxiliary beams. Given that in the case of vertical load to external forces the horizontal reaction or horizontal component is zero, unknown reactions can be easily determined from the conditions of equilibrium of parallel forces and construct a diagram of internal forces according to the general scheme. Influence lines can also be constructed for the required parameters using the appropriate reaction chain. [ABSTRACT FROM AUTHOR]

This article proposes a bridge damage detection method using direct rotation measurements. Initially, numerical analyses are carried out on a one-dimensional (1D) simply supported beam model loaded with a single moving point load to investigate the sensitivity of rotation as a main parameter for damage identification. As a result of this study, the difference in rotation measurements due to a single moving point load obtained for healthy and damaged states is proposed as a damage indicator. A relatively simple laboratory experiment is conducted on a 3-m long simply supported beam structure to validate the results obtained from the numerical analysis. The case of multi-axle vehicles is investigated through numerical analyses of a 1D bridge model and a theoretical basis for damage detection is presented. Finally, a sophisticated 3D dynamic finite element model of a 20-m long simply supported bridge structure is developed by an independent team of researchers and used to test the robustness of the proposed damage detection methodology in a series of blind tests. Rotations from an extensive range of damage scenarios were provided to the main team who applied their methods without prior knowledge of the extent or location of the damage. Results from the blind test simulations demonstrate that the proposed methodology provides a reasonable indication of the bridge condition for all test scenarios. [ABSTRACT FROM AUTHOR]

Summary: The bridge influence line can reflect the structural behavior under moving vehicle loads, which is becoming a proposing tool to study the bridge behavior during service life. A high‐speed vehicle will induce dynamic effect on the bridge, which will significantly change the shape of response curve. It is essential to consider the dynamic response for the accurate identification of the bridge influence line. This paper firstly summarizes current research activities for bridge influence line identification and components of the bridge dynamic responses. A dynamic fluctuation part elimination method based on empirical mode decomposition is proposed, based on which the bridge static influence line is identified by static bridge influence line identification method. Finally, a vehicle–bridge coupling model of a simply supported beam excited by passing vehicles with three different speeds is introduced to illustrate the effectiveness of the proposed identification method. The result of this study provides an effective identification method of the bridge static influence line, which can lead to a satisfactory identification results based on the dynamic responses induced by high‐speed vehicle loads. [ABSTRACT FROM AUTHOR]

In this article, the condition assessment of a cable-stayed bridge using remote sensing is presented. The displacement influence line (DIL) of the bridge under the live load tests is measured for a discrete number of target points. Three different remote sensing techniques including, laser scanning, terrestrial robotic total station and digital levelling are adopted for this purpose. It is demonstrated that DIL obtained by non-contact system is capable of identifying an emulated damage in an actual operating system. The contribution of the work is fourfold. First, a damage index based on the displacement profile of the bridge under the weigh-in-motion is extracted from the non-contact sensing system. Second, our study compares three different remote sensing techniques, namely, digital levelling, robotic total station and laser scanning and uses the measurements to validate the finite element model. Third, the effectiveness of the proposed method for structural damage identification is validated in a real-world large-scale operating structure. Finally, it is validated that strain-based influence line is highly likely to misidentify damage especially when the location of damage is not in the close proximity of the sensor; however, DIL is a better damage indicator even if damage occurs far from the measurement point. [ABSTRACT FROM AUTHOR]

Traffic micro-simulation is the newly developed approach for loading calculation of long span bridges. The approach is quite precise, but computationally expensive to consider the full extent of traffic loading scenarios during a bridge lifetime. To address this shortfall, an efficient multi-scale traffic modelling approach is proposed. The proposed approach uses micro- and macro-simulation with different load model varieties (LMVs), or fidelities (levels of detail) of traffic loading in different bridge regions, to achieve optimal computation efficiency while maintaining the precision of loading calculation. Metrics of influence line (IL) characteristics, such as degree of nonlinearity, are proposed to evaluate the appropriateness of the choice of LMV, and standards of the metrics are also investigated to quantify the implementation of LMVs on bridge IL regions in the multi-scale modelling. Finally, two typical ILs are used along with random traffic modelling to study the feasibility of the proposed approach. It is shown that the multi-scale modelling approach proposed here achieves high computational efficiency and accuracy, which is significant for the massive traffic load simulation for lifetime bridge load effect analysis. [ABSTRACT FROM AUTHOR]

To ensure the safety of cable-stayed bridges, a long-term condition evaluation method has been proposed based on dead load–induced cable forces. To extract dead load–induced cable forces under random vehicle loadings, a novel approach is first developed by integrating influence lines with monitoring data. Then, based on the extracted dead load–induced cable forces, the evaluation algorithm for stay cable systems is presented. In the assessment algorithm, uniform and non-uniform characteristics are taken into account. Finally, the Third Nanjing Yangtze River Bridge, a typical large span cable-stayed bridge, is used to illustrate the effectiveness of the proposed methodology. As a result, the maximum relative error in extraction of dead load–induced cable forces accounts for 4.78% within the studied five stay cables. The precision of the extraction method is acceptable for practical applications since the relative error is less than 5%. Moreover, the bridge is continuously assessed using the dead load–induced cable forces for 5 years. Eliminating the influence of vehicle loadings, the condition of the bridge gradually degrades with time but still remains in good condition. The study not only provides a long-term condition evaluation method for stay cable systems but a dead load–induced extraction approach under random vehicle loadings, which will help bridge owners know well the condition of bridges to make appropriate maintenance decisions. [ABSTRACT FROM AUTHOR]

Highlights • A novel and robust damage identification technique based on the Rotation Influence Line (RIL) is proposed which has not been reported in the literature. • The method only relies on two measurement points at either end of the bridge. • The method is capable of identifying damage, even far from the sensor location. • The validation of the technique is demonstrated through extensive numerical and field test investigations on a statically indeterminate cable-stayed bridge structure. • It is demonstrated that the conventional strain-based measurement is very likely to misidentify cable damage even with extreme case of cable loss. Abstract This study proposes a new damage identification technique for condition assessment of bridge structures. The method is based on the concept of rotation influence line (RIL) at the bridge bearing locations, and solely relies on measurements obtained from two points at either end of the bridge e.g., RIL R and RIL L. The sensitivity of the rotation measurement to damage is first investigated using a 1-D simply supported beam model and it is demonstrated that unlike conventional strain measurements, the rotation measurement is capable of providing useful information about damage even though it is far from the measurement point. Further, an existing cable-stayed bridge is considered to validate the capability of RIL in damage identification. A comprehensive three-dimensional finite element model (FEM) of the bridge is established and calibrated using the measured static and dynamic responses. Numerous field tests are conducted on this large-scale structure to extract static and dynamic characteristics, including natural frequencies, mode shapes and influence lines (ILs). Sixteen hypothetical damage scenarios are induced in the numerical model, including symmetric and asymmetric cases of cable loss. A damage index based on the normalised discrepancy of RIL between the benchmark state and an unknown state is introduced and through extensive investigations it is demonstrated that regardless of damage location, either RIL R or RIL L (or both) can successfully identify the induced damage in all of the sixteen damage scenarios. In contrast, the success of strain-based measurement is highly dependent on the closeness of damage to the sensor location, thus a much higher number of strain gauge sensors is required not to misidentify damage. The contribution of this work is four-fold. First, a novel and robust damage identification technique based on RIL is proposed which has not been reported in the literature. Second, the method solely relies on two measurement points e.g., two tilt meters at either end of the bridge and it is capable of identifying damage, even far from the sensor location. Third, the validation of the technique is demonstrated through extensive numerical and field test investigations on a statically indeterminate cable-stayed bridge structure. Finally, it is demonstrated that the conventional strain-based measurement is very likely to misidentify cable damage even with extreme case of cable loss. [ABSTRACT FROM AUTHOR]

This paper presents equivalent deflection as a new concept for bridge damage detection. The equivalent deflection is similar to the real deflection and can be inferred from bridge acceleration measurements (accelerometer). While calculating measured acceleration, the method essentially explores the static response of the bridge to load. It is laid on the premise that most of the deflection response is generally static rather than dynamic. By using the equivalent deflection from a group a trains without knowing their bogie weights, the average single bogie equivalent deflection response (ASBED) can be calculated and used as an indicator of structural damage. Initially, the theoretical procedure is described to find the equivalent deflection from an acceleration signal using a moving force identification algorithm. Then, numerical analyses are carried out to validate the algorithm. The results confirm that the equivalent deflection is close to the real deflection and contains only a small dynamic component. Then, a method to find the shape of influence line is developed. Using real acceleration data from a 26.8 m simply supported railway bridge (located over the West Coast Mainline in Staffordshire, UK), the shapes of equivalent influence lines are obtained from different groups of trains with very good repeatability. The ASBED response concept is introduced as the product of the 'inferred bogie weight' and the shape of the influence line. Good repeatability of the ASBED response is obtained from site with variability less than 1.5%. Theoretically, this ASBED response should be able to detect changes in the influence line when the average bogie weight at the site is repeatable. The last section explores the changes in the theoretical influence line due to different bridge damage conditions by using a 2D grillage bridge model. The results show that a 2% overall reduction in material stiffness or 20% local reduction (over 1.5 m) should be detectable using this method. [ABSTRACT FROM AUTHOR]

Bridge weigh-in-motion (BWIM) provides an alternative to conventional static weigh station for obtaining vehicle axle weights. Existing BWIM algorithms assume the vehicles being measured are traveling at a constant speed. This is a reasonable assumption for short-span highway bridges, but will yield large error for railroad bridges and bridges subjected to speed varying traffic. This paper presents a static BWIM methodology to improve the estimation accuracy on axle weights when measuring a vehicle traveling at nonconstant speed. A novel speed correction procedure is proposed and incorporated into the BWIM methodology by converting variable speed response data with nonuniform spatial spacing to uniform spacing. This new method can lead to an increased BWIM accuracy for bridges subjected to speed varying traffic. The BWIM methodology proposed in this paper is based on static analysis, and consists of the following components: Speed Detection, Lateral Load Distribution Determination, Influence Line Determination, Speed Correction, and Axle Weight Determination. In this paper, both numerical study and field study have been conducted. The numerical study is performed using finite element analysis to evaluate the performance of the BWIM results when measuring loads traveling at constant speed and variable speed. The results of the numerical study demonstrate the need of applying speed correction for BWIM analysis, and show the proposed speed correction procedure is able to improve BWIM accuracy for a variable speed vehicle to the similar level of accuracy as a constant speed vehicle. Field study is performed on a steel truss bridge located at the University of Alabama campus. With the obtained data on the bridge, the proposed BWIM methodology is applied to determine the axle weights of a vehicle moving at nonconstant speed. Results of the field study show that large error in axle weight prediction is expected when the vehicle is moving at nonconstant speed on the bridge, and correcting the response data using the proposed procedure can significantly improve the accuracy of axle weight predictions. [ABSTRACT FROM AUTHOR]

The bridge influence line (IL) is widely used in structural evaluation, damage identification, model updating, and other applications. The IL curve of an existing bridge is usually different from its designed value. It is crucial, yet challenging, to detect the actual bridge IL by measurement. In this paper, a fitting method is proposed for extracting the bridge IL from the dynamic response induced by a passing vehicle. Based on the theoretical mechanism of bridge IL under different boundary conditions, along with the features of bridge vibration, a piecewise polynomial and a series of harmonic sinusoids are established to describe the IL of the measured point and the fluctuation part of the bridge response, respectively. A mixed function obtained by linear superposition of these factors is utilized to fit the bridge dynamic response. The coefficients of the piecewise polynomial can be extracted by a least-square calculation; and subsequently, the actual IL curve of the measure point of the bridge is obtained. Numerical examples of vehicle-bridge models are adopted to validate the fitting method of IL extraction proposed in this paper, and to illustrate the efficiency by comparing it with the existing direct inverse calculation method. The validity and feasibility of this method are also demonstrated by both a laboratory experiment and a field test. [ABSTRACT FROM AUTHOR]

Applying signal processing tools on quasi-static moving load induced response to localize damage is extremely promising in the field of structural health monitoring. However, it is still an issue to quantify damage for such methods for the lack of definite correspondence between damage severities and damage indexes they adopted. This paper aims to develop a two-stage method with the ability to quantify structural damages by using the quasi-static moving load induced displacement response. As the displacement response of beam structure caused by quasi-static moving load is in close proximity to displacement influence line (DIL), this paper investigates the correspondence between damage parameters and several DIL related features systematically. Then a damage localization index is defined based on area of the region encircled by the DIL change (DILC) and element (ADE), and a damage quantification equation is established based on the area of the region encircled by the DILC and beam (ADB), respectively. Such a two-stage method can take better advantage of the rich test data provided by moving load test, localize and quantify damage with few sensors rapidly. Numerical and experimental examples are carried out to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• An input–output vision-based measurement system was proposed to obtain bridge Displacement Influence Line (DIL). • Experiments for damage detection using the vision-based measurements of DILs was conducted for different damage scenarios. • Random variations in vehicle speed were considered in the process of DIL measurement and damage detection. • The Chordwise Displacement Influence Line was proposed to compensate adverse friction effects in the boundary supports. • A concept of integrating WIM with vision systems was introduced for DIL-based damage detection on operational bridges. This study presents an experimental validation for a high-precision vision-based Displacement Influence Line (DIL) measurement system for a purpose of damage detection on bridges. The vision-based DIL measurement system is a promising tool for structural health monitoring on real operation bridges, which combines two Computer Vision subsystems and weigh-in-motion (WIM) devices. Two vision systems are utilized for tracking vehicle position and measuring structural displacement, while the WIM device obtains vehicle weight information. To demonstrate the feasibility of such a vision-based DIL measurement system, this study developed a vision system using a Go-Pro camera for vehicle positioning and a consumer grade camera for displacement measurement, followed by a series of laboratory experiments on a simply supported bridge using vision-based DILs to assess damage existence and localisation. Five damage scenarios were created by restrengthening the test structure instead of damaging it. Each restrengthened structure was considered intact while the original structure was considered damaged. Vision-based DIL measurements were repeated 12 times for each damage scenario to observe uncertainties in damage localisation as well as DILs. As the measured DILs were found adversely affected by the friction on the boundary supports, the Chordwise Displacement Influence Line (cw-DIL) approach was proposed to compensate for this effect. Damage-induced cw-DILs were shown to be able to assess damage existence and localisation successfully and consistently for all five damage scenarios. [ABSTRACT FROM AUTHOR]

• A scaled beam test was conducted to validate proposed IFCM in influence line extraction. • The proposed IFCM can extract the actual influence line of bridge structures using the dynamic response accurately. • The proposed influence line extraction method can be applied in a flexible testing condition. This paper presents a novel numerical method for accurately extracting bridge influence line (IL) from dynamic responses of general bridge structures. In this method, the IL is approximated by a multi-segment basis function, and a normalized vehicle-induced bridge response is employed as initial IL. Subsequently, an iterative fitting calculation method is proposed to eliminate fluctuation interference in the initial IL curve such that highly accurate actual bridge IL with quasi-static characteristics can be extracted. The feasibility and accuracy of the proposed IL extraction approach for bridges are validated through numerical and experimental studies. Based on which, a parametric study is conducted to identify key parameters affecting the deflection IL extraction performance. The results show that the proposed method can extract the actual bridge IL accurately and efficiently when the segment length is in the range 1/32–1/16 of the bridge span. Compared with conventional methods, the proposed method can extract the actual IL under more flexible testing conditions. Hence, the presented numerical approach is promising for IL-based bridge structure applications. [ABSTRACT FROM AUTHOR]

The longer the design working life be used, the higher possibility of maximal crane load that appears in the actual operation will be. When considering the crane load in combination with action effects, working life should be designed as a time parameter, and use the most unfavorable value. When determining the crane load probability model, lessons are drawn from the theory of stationary binomial random process. On the basis of the probability model (extreme I-type distribution) about any statistical point value in observation period, the probability model (extreme I -type distribution) and statistical parameters about crane load maximum in different design working life are ultimately confirmed. Then, in the load combination, crane load as the dominant load should be taken into account by the most unfavorable case. According to the statistical analysis, on the one hand, the probability is thought of 100% about desirable crane appeared within the influence lines. On the other hand, the position of the crane in influence line is random. The Turkstra rules are amended on the multiple crane load effects combination within the influence line. The probability model of crane load combination effect is determined as extreme I-type, and the statistical parameters are obtained through the error transfer formula calculation. [ABSTRACT FROM AUTHOR]

A novel method was proposed for the moving load identification of bridges based on the influence line theory and distributed optical fiber sensing technique. The method of load and vehicle speed identification was firstly theoretically studied, and then numerical simulation was also performed to study its accuracy and robustness. The numerical results showed that this method was characterized by high accuracy and excellent resistance to noise. Finally, the load identification of an actual continuous pre-stressed concrete beam bridge was carried out with the proposed method. The bridge consists of four pre-stressed box beams. At the same time, a weigh-in-motion system was also installed about 200 m in front of the bridge to measure the speed and moving loads with a purpose of comparing the load identification of the proposed method. Long gauge fiber Bragg grating (FBG) sensors with a gauge length of 1.0 m were adhered to the bottom of the beams. The individual loaded vehicles and the corresponding structure response were mainly monitored as standard samples, and the speed and weight of the sample vehicles were monitored and identified with the proposed method. The results revealed that the distributed long gauge FBG sensors were capable of sensing the structure response precisely and identifying the traffic load. On the basis of the design information and ambient vibration testing results, a refined model was established and the response under unit moving load was acquired for load identification. It was also shown that the sensors in different positions can achieve accurate vehicle speed and weight, the relative error of which are within 10% and 15%, respectively. [ABSTRACT FROM AUTHOR]

Numerous long-span suspension bridges have been built worldwide over the past few decades. To ensure the safety of such bridges and their users during the bridge service life, several bridges have been equipped with Structural Health Monitoring Systems (SHMSs), which measure dynamic bridge responses and various loading types on-site. Integrating SHMS and damage detection technology for condition assessment of these bridges has become a new development trend. Recent studies have proven that stress influence line (SIL)-based damage indices achieve excellent damage detection performance for a long suspension bridge. However, an accurate and prompt manner of identifying the SIL of a long suspension bridge is important to facilitate the development of the SIL for an effective damage index. Identifying the SIL from field measurement data under in-service conditions has several advantages over the traditional static loading test. This study proposes and develops a new SIL identification method by integrating the least squares solution and Weighted Moving Average (WMA) based on the measured train information and the corresponding train-induced stress time history. The efficacy of the proposed method is validated through its application to Tsing Ma Bridge (TMB). The good agreement between the identified and baseline SILs for a typical diagonal truss member verifies the effectiveness of the proposed method. Furthermore, robustness testing is performed by identifying SIL on the basis of information on different trains and train-induced stress responses and by identifying the SIL of different types of bridge components. Results indicate the feasibility of the application of the proposed approach to SIL identification for long-span bridges. [ABSTRACT FROM AUTHOR]

• Curvature is identified from dynamic and quasi-static acceleration response. • Filtering and empirical mode decomposition provide curvature influence lines. • Sparse modal parameters and dense influence lines are fused using Kalman filter. • The system state represents dense and robust curvature estimate. • Monitoring the state evolution allows high-resolution damage localization. Curvature is one of the most popular damage-sensitive features in vibration-based structural health monitoring applications, typically calculated from identified modal features. While the relevant strategic or historical importance of bridges may justify dense sensor networks, a limited budget is generally assigned to monitor "minor" viaducts, thus involving inexpensive devices or extremely sparse sensing solutions. Modal parameters can only be obtained at instrumented locations. Thereby, damage assessment methods based on identified features typically have a low spatial resolution, especially when using low-cost monitoring setups with a modest number of sensing devices. This paper proposes an original identification method for the curvature of bridges based on sparse acceleration measurements that can be collected using standard accelerometers. The raw acceleration signal is processed through a particular filter bank that extracts dynamic and quasi-static signal components. The first components are employed to identify modal parameters, from which sparse yet robust estimates of the structural curvature are retrieved. On the other hand, the quasi-static acceleration generated by the structural deflection induced by traffic load is used to identify the curvature influence lines of the bridge, which are fused with modal estimates using a Kalman filter. The state variable of the analyzed system, representing a dense curvature profile of the structure subjected to concentrated loads, can be used as a damage-sensitive feature for high-resolution damage localization. The method is applied to a steel truss bridge subject to different damage configurations. [ABSTRACT FROM AUTHOR]

• The pattern for train-induced strains of the railway bridge is mapped. • Two condition indicators base on the axial strain in a pair of structural members are given. • The strain pattern is incorporated into the phenomenological model identified from the long-term field data. Detecting abnormal changes in bridge behavior is essential for bridge management. Influence lines are a generally accepted method to describe the quasi-static bridge behavior. Researchers have proposed that the ratio between the influence lines associated structural strains for a pair of sensors can be used as an indicator of abnormal structural condition. However, these methods have been developed for highway bridges; direct application to railway bridges is challenging, because (i) measured responses corresponding to each axle load are coupled, and (ii) train-induced longitudinal forces and bearing restraint produce additional strains in structural members. This research seeks to resolve these challenges to enable application to long-span railway bridges with spherical bearings. First, the concept of influence lines is illustrated using the elastic beam theory, providing an understanding of the underlying mechanisms of the train-induced responses. Accordingly, two condition indicators base on the axial strain in a pair of structural members are given (i.e., the ratio between integral areas of pseudo-static strains and the ratio between average pseudo-static strain amplitude). In addition, the statistical patterns of the indicators are studied using numerical simulation. Subsequently, the strain patterns are illustrated for field monitoring data from a long-span steel truss railway bridge. Herein, the calculations for the condition indicators from strain measurements are presented, and the pre-processing of the indicators is conducted to consider bridge bearing properties. Results show that the condition indicators are naturally clustered according to tracks and the directions of two consecutive trains; each cluster can be represented using a normal distribution. Note that these distributions do not change with the variation of train loads (i.e., train weight and train speed). The recognized pattern provides a potential approach for the identification of abnormal behavior of railway bridges. [ABSTRACT FROM AUTHOR]

This paper describes a procedure for estimating the nonlinear ultimate load carrying capacity of continuous beams using a combination of linear elastic influence lines for segments of the beam in appropriately selected damaged configurations. The method extends the use of influence lines beyond the elastic limits while preserving their inherent efficiency and accuracy. The approach is validated by comparing it to numerical results obtained through structural nonlinear analysis software and other analytical solutions. A main advantage of the method is its ability to determine the most critical locations for placing multiple point loads that cause the beam’s plastic failure. This method may be of practical interest in structural and bridge engineering when numerous nonlinear analyses are required to estimate the ultimate capacity of structures that may be subject to moving loads. [ABSTRACT FROM AUTHOR]

In this paper a procedure for the design of the structures of a generic roller coaster ( RC ) is described. This activity is part of a partnership that the authors are entertaining with one of the major European amusement park industries. The full procedure, developed on theoretical bases and under simplified hypotheses, aims to make designers able to properly and quickly size the complete structure (for complete structure authors mean rails and support structures). In the present paper the design of the rails is presented. The data, needed by this procedure, are the rail track tridimensional geometry and the time histories of contact forces between vehicles and rails. The procedure has been subsequently verified by applying it to an existing RC in order to test all its capabilities and foresee future development and improvement, aiming to insert it in a consolidated general procedure, ever growing and expanding. [ABSTRACT FROM AUTHOR]

Bridge weigh-in-motion (BWIM) systems take influence line (IL) as a reference to calculate axle weights. The calibrated ILs based on field measurements can acquire better conformity with the actual situation and more actually represent the characteristic of existing bridges. Following the methodology proposed by O'Brien et al. (2006), this paper presents back-calculating of the ILs from direct measurements taken on field tests for identification of axle weights of heavy vehicles. The algorithm for calibrating ILs is based on continuously measured bridge responses (strains) produced by two calibration vehicles passing across the instrumented bridge. In terms of the calibrated ILs, Moses' algorithm was applied to identify axle weights of moving vehicles by the least-square method. Field tests on highway US-78 in Alabama were conducted as a case study to evaluate the accuracy of the presented algorithms in calibrating ILs and to identify the axle weights by comparing with the static measurements, and with the measurements by the bending-plate weigh-in-motion (WIM) system on a one-to-one basis to demonstrate the accuracy of BWIM system relative to conventional pavement WIM systems. Finally, factors influencing axle weight identification, including the selection of ILs, the shapes of ILs and the scan numbers for collecting strain signals, were discussed. [ABSTRACT FROM PUBLISHER]

This paper presents a reliable closed form analytical solution without any major simplification which can be used with confidence by designers for the analysis of statically indeterminate horizontally curved bridges idealized as curved girders. The unit load method, a method extracted from the Castigliano's second theorem is used to derive the proposed closed form analytical solution which is then used to obtain the influence line diagram of shear force, bending moment and torsion of the curved bridge girder and it is validated with the published test results and also with the finite element solution of a curved girder. Moreover, an extensive parametric study is conducted using the proposed analytical model to investigate the effect of various parameters such as curvature ratio, span length, number of cells and number of loading lanes on bending moment and torsion of the curved bridges subjected to Australian bridge design loads where the effect of all these parameters except the number of cells is found to be significant. Finally, a comparative study of the behaviour of horizontally curved bridges subjected to loads recommended by three different international bridge design codes is undertaken which concludes that the Eurocode LM1 predicts higher bending moment as well as torsion with respect to other codes. [ABSTRACT FROM AUTHOR]

A new transmissibility-like index defined as the ratio of the frequency responses of the same monitoring location under two different loading conditions was proposed for Gross Vehicle Weights (GVWs) monitoring in this study. Based on the theoretical finding that the displacements of a beam subjected to moving loads were the convolution of the load and the influence line, the equivalence between the transmissibility-like index at the zero frequency and the ratio of two GVWs under two-moving-load scenarios was theoretically revealed. Given the reference responses for known moving loads, an influence line-free algorithm was proposed to estimate the GVW of an arbitrary vehicle by making full use of the unique property of the new transmissibility-like index. To accommodate various uncertainties and fuse the measurements of different channels simultaneously, the problem of Bridge Weigh-In-Motion (B-WIM) was formulated in the framework of Bayesian inference with the aid of a complex Gaussian ratio probabilistic model of transmissibility function. The posterior distribution of the GVW was derived analytically. By applying the proposed transmissibility-like index, this method possessed an obvious advantage in achieving robust GVWs without the requirement of any knowledge of the bridge model such as the influence line. Two applications, including a numerical example and an experimental verification, were used to demonstrate the efficiency and accuracy of the statistical and influence line-free B-WIM scheme. [ABSTRACT FROM AUTHOR]

The influence line for deflection of non-prismatic simply supported beams has been developed. The methodology primarily comprises the determination of the Green's function of the governing differential equation and extension of the results to typical problems. The method of inverse operator along with the orthogonal eigenfunction expansion was employed and the final form of the solution is presented in an integral form, which can be solved by either direct methods or numerical techniques. An example problem and the closed-form solution for a particular class of non-prismatic beams, very often applied in practice, have been presented. [ABSTRACT FROM AUTHOR]

This paper presents a new method of identifying dynamic vehicular axle loads by directly using bridge measurements. To demonstrate how the proposed methodology works out, vehicle-bridge coupling equations are first established for numerical simulations. The developed vehicle-bridge coupled system contains only the modal properties of the bridge and the physical parameters of the vehicle. Using the superposition principle and influence surface concept, the dynamic interaction forces (i.e. the dynamic axle loads) can then be identified from the measured bridge responses. A series of simulations are carried out in which the effects of various factors such as bridge inertia force, measurement station, vehicle speed, traveling route, number of vehicles, road surface condition, and noise level have been investigated. The identified results show that the proposed method is able to identify dynamic axle loads with good accuracy, which can serve as a basis for dynamic bridge-weigh-in-motion. This paper also serves as a theoretical base for the companion paper in which the proposed method is applied to identify the dynamic axle loads of a real truck on an existing bridge. [ABSTRACT FROM PUBLISHER]

• A flexibility matrix identification method by using the moving vehicle induced responses for beam type bridges. • Proportional relationship between flexibility matrix and matrix formulated by influence lines of several points is derived. • The flexibility matrix is formulated based on the measured influence lines and the derived proportional relationship. • It can identify the flexibility matrix and estimate the static displacement with high accuracy and efficiency at the cost of few sensors. Bridge flexibility matrix is widely used in damage detection and condition assessment. As the mass-normalized mode shapes with high spatial resolution are required in formulating the flexibility matrix, the difficult tasks of measuring excitation and arranging a large number of sensors are normally involved. This paper proposes a flexibility matrix identification method by using the moving vehicle induced responses for beam type bridges. Firstly, the proportional relationship between the modal flexibility matrix and the matrix formulated by the bridge influence lines (ILs) of several points is derived theoretically. Then the quasi-static component response of the moving vehicle induced responses is extracted through the analytical mode decomposition (AMD), and the IL of the measurement point is obtained via polynomial fitting accordingly. Finally the flexibility matrix is formulated based on the measured ILs and the derived proportional relationship, and employed to estimate the static displacement of the bridge. Numerical and experimental examples are conducted to verify the accuracy and feasibility of the proposed method. The results indicate that it can identify the bridge flexibility matrix and estimate the static displacement with high accuracy and efficiency at the cost of few sensors. Besides, the proposed method is insensitive to vehicle parameters (velocity, spring stiffness, wheelbase, and weight), road surface roughness and measurement noise. [ABSTRACT FROM AUTHOR]

This paper focuses on implementing existing bridge weigh-in-motion technology on railways. Using only simple instrumentation with four concrete embedded sensors, a complete WIM system with axle detection, weighing, track detection and attempted identification of locomotives was implemented on a bridge with multiple railway tracks. The results and the effectiveness of this type of sensor setup is discussed. [Copyright &y& Elsevier]

• A novel Bayesian model updating method was developed using Python–Abaqus interface. • The new method was employed for the model updating of Ting Kau Bridge. • The effect of model class on the results of model updating is studied. • The full-scale vehicular load test was designed and implemented. • The updated FEM is verified using the vehicular load test results. Bayesian model updating framework provides a reliable method for building high-fidelity finite element models (FEMs). To realize the efficient model updating of large-scale civil engineering structures, a practical Bayesian inference framework based on software interaction is proposed. The newly developed framework was applied to update the FEM of a long-span cable-stayed bridge, Ting Kau Bridge in Hong Kong, utilizing measured modal parameters from the literature. The model updating results are found to be highly sensitive to the selection of model classes. Furthermore, the area of the main girder of the bridge deck is a key parameter influencing the lower modes of the cable-stayed bridge. A full-scale vehicular load test is conducted on the Ting Kau Bridge to obtain the displacement influence line through the data recorded by GPS sensors on the bridge. The set of measured influence lines is employed to verify the accuracy of the updated FEM. The results demonstrate that the characteristics of the FEM updated using the proposed Bayesian model updating framework based on measured dynamic properties are consistent with the structural characteristics of the bridge. The proposed framework can facilitate the structural health monitoring of large-scale civil engineering structures. [ABSTRACT FROM AUTHOR]

• Cyber-physical system collects matched bridge input (truck loads) and output data. • Site-specific load rating parameters derived from bridge responses and truck weight data. • Data-driven load rating method leads to more accurate rating factors. • Two operational bridges rated using proposed method for evaluation. Load rating is a widely used approach for evaluating the load-carrying capacity of bridges in an effort to ensure safe bridge operation under expected traffic loads. Load rating often relies on simplified analytical models including empirically derived model parameters that do not reflect bridge-specific information resulting in conservative ratings. To reduce this conservatism, this study proposes a novel data-driven framework that utilizes long-term bridge response data to extract bridge-specific model parameters that can be used within in the Load and Resistance Factor Rating (LRFR) process. The data-driven LRFR (DD-LRFR) framework is empowered by a cyber-physical system (CPS) architecture that uses Internet connectivity to integrate measured bridge responses with truck weights measured by a weigh-in-motion (WIM) station. The CPS architecture uses computer vision of camera images to confirm trucks observed at a WIM station are identical to those observed at a bridge. Bridge response and axle weight data are then used to extract probabilistic models of dynamic load allowances and unit influence lines. The DD-LRFR method is validated using a 20-mile (32.2-km) segment of the I-275 northbound highway in Michigan that is monitored continuously by the CPS architecture. Six girders associated with two bridges along I-275 are rated using the proposed DD-LRFR methodology with rating factors compared to those obtained using conventional and refined load rating methods. The DD-LRFR method yields inventory- and operational-level rating factors that are less conservative than those from the approximate LRFR method and comparable to those using finite element modeling of the bridge. [ABSTRACT FROM AUTHOR]

• Proposed method shows good performance on vehicle parameters identification under random traffic flow. • The effects of the vehicle number, road surface condition and effect of vehicle acceleration are investigated. • Proposed method has been verified by bridge-vehicle coupling system and laboratory experiment. Accurate traffic conditions and their development regularity can help in the management of future traffic demand and bridge design. This study proposes a bridge weigh-in-motion method for identifying the vehicle parameters of motorway bridges considering random traffic flow and capitalizes on the advantages of long-gauge fibre Bragg grating sensors. A bridge-vehicle coupling system is numerically simulated, a 1:10 indoor experiment is used to verify the proposed method, and the vehicle parameters (e.g., traffic lane, vehicle spacing, vehicle axle weight, and speed) are identified with high accuracy. The effects of the road surface conditions, vehicle numbers, lateral positions and accelerations on the proposed method are investigated, and only find that the lateral positions are obviously related while and others have almost no influence on the method. In addition, the accuracy in axle number and weight identification is not high when the gauge length is smaller than vehicle wheelbases. [ABSTRACT FROM AUTHOR]

• A novel technology is proposed for bridge influence line identification. • The inverse interval analysis and the support vector machine algorithm are utilized in the method. • The effectiveness of the proposed method is validated by numerical simulations and laboratory tests. • The method can serve as an effective tool with low cost and high efficiency. On-site bridge influence line (IL) calibration traditionally relies upon attached sensors and the pre-weighing of trucks, which limits its popularity for engineering applications. This paper proposes a novel technology for bridge IL identification based on big data of pre-defined statistical vehicle information and interval analysis using the affine arithmetic (AA) method. By employing computer vision techniques, the individual axle-weight intervals were determined. The AA algorithm was used to obtain the interval solutions due to its high computation efficiency (only consumes 1 s). The binary classification algorithm for the support vector machine was adopted to extract the certain IL from the massive amount of IL interval data. The proposed method was verified by employing vehicle-bridge coupled numerical simulation and laboratory tests. Calculations results indicated that the errors of the distinguished IL obtained by the proposed method ranged from 7.43% to 15.16%. The ILs were successfully identified in multiple cases. [ABSTRACT FROM AUTHOR]

Widespread monitoring of bridges is yet rarely employed at a territorial level due to the high costs of monitoring systems. However, the aging of civil infrastructures, combined with the growing traffic demand, poses the need for a simple and automatic tool that helps emergency management. In this paper, an integrated algorithm for the identification of dynamic and dense quasi-static structural features exploiting moving vehicles is proposed. Filtering raw acceleration structural responses, triggered by passing vehicles, enables the identification of modal parameters and curvature influence lines. The procedure can be implemented efficiently as its main computational core consists of convolutions. The definition of a curvature-based damage index leads to accurate localization and quantification of structural anomalies using few sensors. The proposed procedure is tested on three viaducts of the Italian A24 motorway. Moreover, a numerical model is studied to evaluate the potentialities of the strategy for damage localization. • An integrated method to identify modal parameters and influence lines is proposed. • Dense structural features of bridges are identified by filtering acceleration data. • Quasi-static features can be directly employed as dense damage indicators. • Accurate damage localization can be achieved efficiently, using few sensors. • Numerical and experimental case studies show the potential of the method proposed. [ABSTRACT FROM AUTHOR]

• Estimation of vehicle-induced displacement response and influence line using radar device under moving vehicle. • Displacement calculation using simulated static load test scenario. • Estimation of load rating factor and load carrying capacity from moving vehicle testing. • Full-scale verification of load rating factor in a PC girder bridge using both static load test and truck pass-by test. • Displacement estimated by the truck pass-by test is reasonably accurate compared to the conventional static load test. Efficient and reliable load-carrying capacity evaluation of deficient bridges is essential for bridge maintenance and management. Based on load-carrying capacity evaluation of an existing bridge, operator can formulate strategies and implement countermeasures such as repair, strengthening or traffic regulation. Currently, static load test is used widely for evaluation of bridge load-carrying capacity. The test, however, is often considered expensive and requires massive traffic closure that hinders frequent implementation on highway or bridges in the rural area. In this paper, an efficient method for load-carrying capacity evaluation of girder bridges based on displacement under moving vehicle is proposed. The method consists of three main procedures, namely, radar-based dynamic displacement measurement under truck pass-by test, influence line extraction, and estimation of the displacement under the static load test scenario. Based on these procedures, evaluation of load-carrying capacity is conducted following the AASHTO manual. The method was verified in numerical simulations using three-dimensional finite element model of typical girder bridge under vehicle loading. Effects of vehicle weight, vehicle speed, and road roughness on the accuracy of displacement estimation were investigated in the numerical simulations. Full-scale implementation of the method was conducted on a simply supported prestressed concrete girder bridge to investigate feasibility of the proposed method and to provide guidance for practical application. The results reveal that load rating factor of the girders can be reliably evaluated. The evaluation process was also more effective and economical than the conventional method. [ABSTRACT FROM AUTHOR]

A new method was proposed for the damage identification of box girder bridges under moving load, wherein the difference of strain influence line (DSIL) was taken as an index to represent the long-gauge strain difference before and after damage. The damage identification theory based on long-gauge strain influence lines was derived for box girder bridges with shear lag effect under consideration, and a regularized index DSIL was proposed for the quantitative identifications of damage location and extent. A series of experiments were carried out to study the influences of speed, vehicle type, and vehicle weight on the damage identification, and the experimental data were obtained by long-gauge fiber Bragg grating strain sensors. Moreover, numerical simulations were performed to confirm the method. The experimental and numerical results show that the method can locate the damage accurately, and quantitatively identify the damage extent under different working conditions. The experimental damage extent is generally slightly higher than the theoretical, with an average identification error smaller than 5%. Additionally, the relative error of damage extent is smaller than 3% under different working conditions. Thus, the effectiveness of this method was verified. [ABSTRACT FROM AUTHOR]

• Static transmissibility under moving loads are equal to the ratio of Fourier transform of influence lines; • Influence lines can be extracted directly by using the static transmissibility subjected to moving loads; • A fast Bayesian inference scheme is developed to accommodate uncertainties; • The analytical solutions of the MPVs and variances of influence lines in frequency domain are derived; • The closed-form transformation between the MPVs (or variances) of FFT and those of inverse FFT are established. On the basis of the amazing theoretical finding that there is equivalence between the static transmissibility subjected to moving loads and the ratio of two influence lines in the frequency domain, a new approach is proposed to extract the influence lines for a beam-like structure under moving loads. To accommodate the uncertainties involved in the measurements as well as modelling error, the relationship between response measurements and the Fourier transform of influence lines is embedded in the framework of Bayesian inference with the aid of complex-valued probabilistic model of prediction error. The formulas are presented for closed-form transformation between the solutions of FFT coefficients and those of inverse FFT coefficients. Analytical solutions of the Most Probable Values (MPVs) as well as posterior uncertainties of the influence lines in both frequency domain and spatial domain are derived. Two applications are conducted to verify the efficiency and accuracy of the fast Bayesian scheme. It is shown that the new approach can be realized by avoiding the ill-poseness nature of inverse problem. Due to the introduction of the concept of static transmissibility, given that the reference influence line is known in advance, this method possesses an obvious advantage in avoiding using the knowledge of the moving loads. As a frequency-domain approach, it can reduce the computational complexity of influence line extraction by avoiding complicated matrix manipulation. [ABSTRACT FROM AUTHOR]

• Rotation is a sensitive parameter for identifying damage in a bridge. • Rotation can be accurately measured using a high-grade DC accelerometer. • Difference in rotation influence lines for healthy and damaged states successfully identifies damage in a model bridge. • For simply supported bridges, supports are optimum sensor locations for damage identification. This paper presents a novel bridge condition assessment methodology using direct rotation measurements. Initially, numerical analyses are carried out to develop the theoretical basis of the proposed bridge damage detection methodology. As a result of this study the difference in rotation influence lines obtained for healthy and damaged bridge states is proposed as a damage indicator. The sensitivity of rotation to damage and the effect of sensor locations on sensor sensitivities are investigated. Subsequently, extensive laboratory experiments are conducted on a 5.4 m long simply supported bridge structure in an effort to validate the results from the numerical analyses. The test structure is instrumented with rotation sensors and axle detectors and loaded with a four-axle moving vehicle. In this study, rotations are measured using high grade uniaxial accelerometers. The procedure of measuring rotations using accelerometers is explained within the scope of this study. To test the robustness of the proposed bridge condition assessment methodology, a wide range of single and multiple damage scenarios is investigated and the results from this study show that the proposed methodology can successfully identify damage on a bridge. For the model considered, damage as low as 7% change in stiffness over an extent of 2.5% bridge span is shown to be detectable. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm that estimates the influence line (IL) of a bridge using data collected when trucks pass over the sensors installed in the bridge. The algorithm is tested with data collected from the Millau Viaduct in France using a bridge weigh-in-motion (B-WIM) device. The algorithm uses the maximum likelihood estimation (MLE) and is compared with an old algorithm. The algorithm is more robust because it takes into account many signals for the estimation of the IL. [ABSTRACT FROM AUTHOR]