Structural Health Monitoring provides ample opportunity for deep analysis of our infrastructure, including using the bridge as a scale through a process called (Bridge) Weigh in Motion (BWIM). Many variables impact the BWIM’s capabilities, accuracy, and by extension, overall usefulness. The overall goal of the research conducted was to identify methods of improving the accuracy and performance of BWIM. That goal was narrowed down to two specific objectives: 1) assess if a higher sampling rate leads to increased BWIM performance as postulated by some sources [1]; and 2) attempt to develop a means of analyzing complex multi-vehicle events where the distributed strain profile cannot be processed by the standard BWIM algorithms. The first objective was accomplished using a Matlab simulation to generate sampled strain signals, with different sampling nonidealities such as offsets in the start of captured events. The testing demonstrated that the sampling rate is sufficient at 100 Hz, with minute peak detection errors manifesting only when running at unreasonable levels of accuracy for the Matlab analysis. Given that information, there is potentially room for reducing the sampling rate which benefits BWIM installations by saving on data storage requirements. Beyond that, no further testing is recommended in the area of sampling rate. The second objective was accomplished by using higher-order signal processing techniques such as Independent Component Analysis (ICA). These techniques aim to, at a minimum, ensure that heavy-vehicle events are detected and recorded. A total of four tests were performed on Independent Component Analysis — two on simulated strain mixtures and two on signal samples collected from bridge data. The results of the tests demonstrate ICA may potentially be introduced into a BWIM implementation pending further refinements. The most likely targets for improvement are through analyzing the independence of truck signals using correlation, taking measures to decorrelate the mixtures, and also testing whether post-separation filtering of the strain readings impacts the result. Notwithstanding those areas of improvement, the overall verdict is that a clear recommendation for using ICA in active BWIM analysis is not currently feasible. / October 2015

This degree project aims to provide the Swedish Traffic Administration with information about the pressure distribution generated by vehicles with gross weights above 3.5 tons. The aim is to provide sufficient information regarding the pressure distribution to facilitate a possible change of today's deterministic design model to a probability based method. To realize the aim, BWIM-measurements carried out by the Swedish Traffic Administration in 2015, 2016 and 2017 have been studied. Measured data showed large variations depending on vehicle type, indicating that the data originates from different statistical populations. For the surveyed years, the data of interest are vehicles loaded to at least 80% of their permitted gross weight and a lognormal distribution has been found to be best representative of the pressure from traffic loads. The studied data also showed that the magnitude of the pressure increases with reduced vehicle length and that shorter vehicles whom are more frequently overloaded, subjects the structure with a pressure magnitude as great as twice the design value of today's 20 kPa. While plotting the pressure distribution, a positive skewness was observed. This indicates that the majority of observed vehicles load within the legal boundaries of their permissible gross weight. The vehicles that generate extreme pressures therefore represent a small fraction of all vehicles. Based on the studied vehicles the characteristic value of the 98\% fractile was calculated to 18,5 kPa. For the theoretical case where overloading was non-existent, this value decreases with 5% / Detta examensarbete syftar till att förse Trafikverket med information om tryckfördelningen genererat av fordon med bruttovikter överstigande 3,5 ton. Syftet är att bidra med tillräcklig information för att underlätta en eventuell förändring av dagens deterministiska dimensioneringsmetod till en sannolikhetsbaserad metod.För realiseringen av frågeställningen har BWIM-mätningar utförda av Trafikverket för åren 2015,2016 och 2017 studerats. Mätdatan har visat sig variera kraftigt beroende på fordonstyp och slutsatsen har därmed dragits att mätdatan härstammar från olika statistiska populationer. För undersökta mätår har fordon lastade till minst 80% av sin tillåtna bruttovikt undersökts och en lognormal fördelning har visat sig mest representativ för trafiklasten. Vidare påvisade mätdatan att storleken på trycket ökar med minskad fordonslängd och överlast av kortare fordon kan utsätta vägkroppen för tryck upp till dubbla storleken av dagens dimensioneringsvärde på 20 kPa. Vid plottning av tryckfördelningen märks en tydlig positiv skevhet vilket påvisar att merparten av fordonen lastar inom ramen för sin tillåtna bruttovikt och att extrema tryck endast utgör en liten procentandel av alla fordon. Baserat på de undersökta fordonen har den karakteristiska lasten för de undersökta åren beräknats till 18,5 kPa och att detta värde sänks med 5% om överlast inte varit ett faktum.

Bridges are vital components of the U.S. transportation network. However, every year, the transportation agencies report a large number of aging bridges that are structurally damaged. Also, evolving traffic and particularly the overloaded traversing traffic can threaten the bridges' integrity and safety further. Bridge weight-in-motion (BWIM) is a system that takes the instrumented bridges as a scale and uses the structure response to compute the trucks' weights with no interruption in the traffic. In a particular type of BWIM, called nothing-on-road BWIM (NOR-BWIM), only a few weighing sensors should be installed under the bridge top slab. Since nothing will be installed on the road surface, NOR-BWIM addresses some of the main challenges of pavement-based WIM and traditional BWIM systems. These include lane closure, interruption to the traveling traffic, and sensitivity to daily tire impacts and harsh weather conditions. It also provides a portable solution with a less labor-intense installation process. Additionally, previous studies have shown that BWIM systems are versatile candidates for overcoming the critical challenges of structural health monitoring (SHM) across various types of bridges. The integration of the two systems is more cost-effective with improved performance; thus, it is more attractive to practitioners. However, the current BWIMs have serious shortcomings that make the integrated SHM-BWIM systems impractical in real-world long-span bridges. In the first two phases of this study, these shortcomings are addressed and a novel BWIM system is proposed. Then, the novel BWIM system is used for SHM in the third phase of the study. These shortcomings are explained as follows. Most studies are performed on short/medium-span T-beam and slab-on-girder bridges. However, longer span lengths, construction methods, different slab properties (e.g., stiffness), etc., can affect the efficacy of the NOR-BWIM. Thus, there is a need to further evaluate this technique on other bridges, such as concrete-box-girder bridges with longer spans, in an effort to ascertain whether or not NOR-BWIM systems would still work effectively on such bridges. Thus, the first phase presents an experimental investigation conducted for a long-span concrete-box-girder bridge (144 m span) called the Smart Road bridge. A total of 18 experimental tests were performed on the bridge. Moreover, a cost-effective sensor placement was developed. It was found that the number of axles is detectable with an accuracy of 100%. Moreover, the estimated mean-absolute-error for axle spacing, vehicle speed, and gross vehicle weight were 4.6%, 2.6%, and 4.6%, respectively. Lastly, it was also demonstrated that the developed cost-effective NOR-BWIM system is capable of lane identification and truck position detection. The second main issue with the existing BWIM approaches is their limited suitability for simultaneous multiple-vehicle cases on multiple-lane bridges. To address this limitation, in the second phase of this study, a novel BWIM approach is proposed. The approach is built around the removal of the non-localized portion of the strain response. Keeping the localized portion of the strain response, which is not sensitive to nearby loads, allowing for enhanced detection. The superiority of this approach stems from its capability to handle multiple-vehicle cases. These may present with an arbitrary number of trucks and light-weight vehicles, simultaneously passing the bridge in any arbitrary pattern or configuration. To show the applicability of the approach, a finite element (FE) model of a long-span concrete-box-girder bridge was simulated. The model was validated against the experimental data collected under known large events. The FE model was then used to consider single-truck events (for proof-of-concept) as well as complex multiple-truck traffic cases. These included in-one-row trucks, zigzag patterns, side-by-side trucks, and a combination of several trucks with several light-weight vehicles present. The results demonstrated that the proposed BWIM approach is capable of detecting the axle weights and gross vehicle weight (GVW) of the traversing trucks. Based on all complex multiple-truck cases, the overall mean absolute errors for GVW and axle weight estimations were 4.5% and 11.3%, respectively. In the last phase, a multiple-presence dual-purpose (MPDP) SHM approach was proposed to monitor the integrity of bridges using the BWIM system existing sensors. This approach centers on the influence line (IL) change and uses a developed multiple-presence IL (MP-IL) technique (in the second phase) for SHM application. This can effectively handle the multiple presence issue of the current integrated SHM-BWIM systems to make them more practical. Also, unlike many SHM-BWIM studies, noise and transverse position change (defined as false damage indicators) were included in the proposed procedure to provide a more realistic bridge health monitoring approach. To show the applicability of the approach, a similar FE model simulated in the second phase was used. The model was then used to evaluate the MPDP approach under single and multiple truck events. Eleven damage scenarios were simulated, and three SHM trucks (a 3-axle, a 4-axle, and a 5-axle) were used to improve the SHM accuracy. Also, an updated sensor placement was proposed to effectively work for both BWIM and SHM applications in both single and multiple-truck events. According to the results, the MPDP SHM procedure coupled with the novel MP-IL and the proposed sensor placement could effectively detect the damage scenarios in both single and multiple-truck events. Also, it was shown that using several independent SHM trucks can make the monitoring process more effective. / Doctor of Philosophy / Every year, the transportation agencies report a large number of aging bridges that are structurally damaged. Also, evolving traffic and particularly overloaded traffic can threaten the bridges' integrity and safety further. Bridge weight-in-motion (BWIM) is a traffic system that takes the instrumented bridges as a scale and uses the structure response to compute the trucks' weights with no interruption in the traffic. In a particular type of BWIM, called nothing-on-road BWIM (NOR-BWIM), only a few weighing sensors should be installed under the road surface. Since nothing will be installed on the road surface, NOR-BWIM addresses some of the main challenges of pavement-based WIM and traditional BWIM systems. These include lane closure, interruption to the traveling traffic, and sensitivity to daily tire impacts and harsh weather conditions. It also provides a portable solution with a less labor-intense installation process. Additionally, previous studies have shown that BWIM systems are versatile candidates for overcoming the critical challenges of structural health monitoring (SHM) across various types of bridges. The integration of the two systems is more attractive to practitioners because it brings improved performance at a lower cost. However, the current BWIMs have serious shortcomings that make the integrated SHM-BWIM systems impractical in real-world long-span bridges. In the first two phases of this study, these shortcomings are addressed and a novel BWIM system is proposed. Then, the novel BWIM system is used for SHM in the third phase of the study. These shortcomings are explained as follows. Most studies are performed on short/medium-span bridges with particular types of structures. However, longer span lengths, construction methods, different bridge components' properties, etc., can affect the efficacy of the NOR-BWIM. Thus, there is a need to further evaluate this technique on other bridges with longer spans and different structural systems to ascertain whether or not NOR-BWIM systems would still work effectively on such bridges. Thus, the first phase presents an experimental investigation conducted for a long-span concrete-box-girder bridge (a different structural system than the literature) with 144-m spans. A total of 18 experimental tests were performed on the bridge. Moreover, a cost-effective sensor placement was developed. It was found that the number of axles is detectable with no error. Moreover, the estimated error for axle spacing, vehicle speed, and gross vehicle weight were all low. Lastly, it was also demonstrated that the developed cost-effective NOR-BWIM system is capable of lane identification and truck position detection. The second main issue with the existing BWIM approaches is their limited suitability for simultaneous multiple vehicles on multiple-lane bridges. To address this limitation, in the second phase of this study, a novel BWIM approach is proposed. The superiority of this approach stems from its capability to handle multiple-vehicle cases. These may present with an arbitrary number of trucks and light-weight vehicles, simultaneously passing the bridge in any arbitrary pattern or configuration. To show the applicability of the approach, a model of the long-span bridge was simulated. The model was validated against the experimental data collected under known traffic events. The model was then used to consider single-truck events and complex multiple-truck traffic cases. The results demonstrated that the proposed BWIM approach can detect the axle weights and gross vehicle weight (GVW) of the traversing trucks. Based on all complex multiple-truck cases, the overall errors for GVW and axle weight estimations were 4.5% and 11.3%, respectively. In the last phase, a novel SHM approach was proposed to monitor the integrity of bridges using the existing sensors for BWIM. This approach uses the proposed BWIM system for SHM application. This can effectively handle the multiple presence issue of the current integrated SHM-BWIM systems to make them more practical. Also, unlike many SHM-BWIM studies, noise and transverse position change were included in the proposed procedure to provide a more realistic bridge health monitoring approach. A similar model simulated in the second phase was used to show the applicability of the approach. The model was then used to evaluate the MPDP approach under single and multiple truck events. Eleven damage scenarios were simulated. Also, an updated sensor placement was proposed to work effectively for both BWIM and SHM applications in single and multiple-truck events. According to the results, the proposed SHM procedure coupled with the novel BWIM and the proposed sensor placement could effectively detect the damage scenarios in both single and multiple-truck events.

Vehicle over loading, or single axle over loading, is one of the major causes of pavement deterioration. Trafik Verket (TV), the Swedish Transport Administration, recognized that the current process for estimating traffic volume should be reevaluated, and if possible improved. This degree project uses data from the Bridge Weigh in Motion (BWIM) system to study the actual loads applied to Swedish highways. The axle load spectrum is plotted with the conventional frequency distribution plots, and with a new cumulative distribution approach. The paper introduces the maximum allowable potential vehicle weight MAPVW concept, and uses this visual technique to identify overloads for different vehicle geometries. The paper concludes that for 5 and 6 axle trucks the triple axle is frequently overloaded, while for longer trucks one of the dual axles is often over loaded. The highest over loads tend to be on the driving axle, suggesting incorrect loading procedures.

When designing roads it’s important to know what loads will be driven on it. The axles on heavy vehicles can either have super single tires or dual tires which, according to studies, damage the roads differently. The Swedish Transport Administration is developing an understanding for the character of these different kinds of axle and tire types on the loads induced on Swedish roads. In this thesis a field study was conducted to determine the distribution between axles with super single tires and axles with dual tires on heavy vehicles. A highway, a country road and a national road were investigated during the study. The result showed that the number of trucks with single tire axles, i.e. those with super single tires, varies between 39 and 48 percent on the different types of road. That the truck has a single tire axle means that the truck has at least one axle with single tires in addition to the steering axle. A small study was conducted to determine the width of tires on heavy vehicles. A comparison of trucks with single- and dual tire axles was carried out in terms of degradation of the roads. Permanent deformation of unbound layers and fatigue cracking were investigated since these degradation mechanisms are currently used as design parameters in the design of roads. Load values from Bridge Weight In Motion (BWIM) data were used in the calculations and vehicle type 113, 123 and 12211 were investigated. Permanent deformation and fatigue cracking were calculated according to the criteria given in ATB VÄG 2005. Dissipated Creep Strain Energy (DCSE) has been calculated for the selected truck types. The study showed that trucks with axles with single tires accelerate the degradation of the roads. The permanent deformation was almost the same for the two different kinds of axle types.

The field of bridge monitoring is one of rapid development. Advances in sensor technologies, in data communication and processing algorithms all affect the possibilities of Structural Monitoring in Bridges. Bridges are a very critical part of a country’s infrastructure, they are expensive to build and maintain, and many uncertainties surround important factors determining their serviceability and deterioration state. As such, bridges are good candidates for monitoring. Monitoring can extend the service life and avoid or postpone replacement, repair or strengthening works. The amount of resources saved, both to the owner and the users, by reducing the amount of non-operational time can easily justify the extra investment in monitoring. This thesis consists of an extended summary and five appended papers. The thesis presents advances in sensor technology, damage identification algorithms, Bridge Weigh-In-Motion systems, and other techniques used in bridge monitoring. Four case studies are presented. In the first paper, a fully operational Bridge Weigh-In-Motion system is developed and deployed in a steel railway bridge. The gathered data was studied to obtain a characterization of the site specific traffic. In the second paper, the seasonal variability of a ballasted railway bridge is studied and characterized in its natural variability. In the third, the non-linear characteristic of a ballasted railway bridge is studied and described stochastically. In the fourth, a novel damage detection algorithm based in Bridge Weigh-In-Motion data and machine learning algorithms is presented and tested on a numerical experiment. In the fifth, a bridge and traffic monitoring system is implemented in a suspension bridge to study the cause of unexpected wear in the bridge bearings. Some of the major scientific contributions of this work are: 1) the development of a B-WIM for railway traffic capable of estimating the load on individual axles; 2) the characterization of in-situ measured railway traffic in Stockholm, with axle weights and train configuration; 3) the quantification of a hitherto unreported environmental behaviour in ballasted bridges and possible mechanisms for its explanation (this behaviour was shown to be of great importance for monitoring of bridges located in colder climate) 4) the statistical quantification of the nonlinearities of a railway bridge and its yearly variations and 5) the integration of B-WIM data into damage detection techniques. / <p>QC 20140910</p>

The field of monitoring is one of rapid development. Advances in sensor technologies, in data communication paradigms and data processing algorithms all influence the possibilities of Structural Health Monitoring, damage detection, traffic monitoring and other implementations of monitoring systems. Bridges are a very critical part of a country’s infrastructure, they are expensive to build and maintain, and many uncertainties surround important factors determining the serviceability and deterioration of bridges. As such, bridges are good candidates for monitoring. Monitoring can extend the service life and avoid or postpone replacement, repair or strengthening work. Many bridges constitute a bottleneck in the transport network they serve with few or no alternative routes. The amount of resources saved, both to the owner and the users, by reducing the amount of non-operational time can easily justify the extra investment in monitoring. This thesis consists of an extended summary and three appended papers. The thesis presents advances in sensor technology, damage identification algorithms and Bridge Weigh-In-Motion techniques. Two case studies are carried out. In the first a bridge and traffic monitoring system is implemented in a highway suspension bridge to study the cause of unexpected wear in the bridge bearings. In the second a fully operational Bridge Weigh-In-Motion system is developed and deployed in a steel railway bridge. The gathered data was studied to obtain a characterization of the site specific traffic. / QC 20111122