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.