The use of timber–concrete composite (TCC) bridges in the United States dates back to approximately 1924 when the first bridge was constructed. Since then a large number of bridges have been built, of which more than 1,400 remain in service. The oldest bridges still in service are now more than 84 years old and predominately consist of two different TCC systems. The first system is a slab-type system that includes a longitudinal nail-laminated deck composite with a concrete deck top layer. The second system is a stringer system that includes either sawn timber or glulam stringers supporting a concrete deck top layer. The records indicate that most of the TCC highway bridges were constructed during the period of 1930–1960. The study presented in this paper discusses the experience and per-formance of these bridge systems in the US. The analysis is based on a review of the relevant literature and databases complemented with field inspections conducted within various research projects. Along with this review, a historical overview of the codes and guidelines available for the design of TCC bridges in the US is also included. The analysis undertaken showed that TCC bridges are an effective and durable design alternative for highway bridges once they have shown a high performance level, in some situations after more than 80 years in service with a low maintenance level.
This paper focuses on evaluating the effectiveness of an unmanned aerial vehicle (UAV) as a supplementary bridge damage quantification tool. For this study, a glued-laminated timber arch bridge in South Dakota was selected, and an UAV was utilized for the bridge damage quantification. A recommended four-stage UAV-enabled bridge damage quantification protocol involving image quality assessment and image-based damage quantification was developed. A field application using the UAV to measure crack lengths, thicknesses, and rust stain areas of the selected bridge was conducted following the recommended protocol. The image quality parameters, including sharpness and entropy, were used to determine the quality of the UAV-captured images. Pixel- and photogrammetry-based measurements using the high-quality images were obtained to quantify the bridge damage, and the damage was compared to that from actual field measurements. Once the damage information was gathered, the UAV image–based damage level classification was established based on the damage levels defined by current standards. The findings confirmed the accuracy of the recommended protocol, with results within 3.5, 7.9, and 14.9% difference for crack length, thickness, and rust stain area, respectively, when compared with the field measurements.
Nationwide, bridges are deteriorating at a rate faster than they can be rehabilitated and maintained. This has resulted in a search for new methods to rehabilitate, repair, manage, and construct bridges. As a result, structural health monitoring and smart structure concepts have emerged to help improve bridge management. In the case of timber bridges, however, a limited amount of research as been conducted on long-term structural health monitoring solutions, and this is especially true in regards to historic covered timber bridges. To date, evaluation efforts of timber bridges have focused primarily on visual inspection data to determine the structural integrity of timber structures. To fill this research need and help improve timber bridge inspection and management strategies, a 5-year research plan to develop a smart timber bridge structure was undertaken. The overall goal of the 5-year plan was to develop a turnkey system to analyze, monitor, and report on the performance and condition of timber bridges. This report outlines one phase of the 5-year research plan and focuses on developing and attaching moisture sensors onto timber bridge components. The goal was to investigate the potential for sensor technologies to reliably monitor the in situ moisture content of the timber members in historic covered bridges, especially those recently rehabilitated with glulam materials. The timber-specific moisture sensors detailed in this report and the data collected from them will assist in advancing the smart timber bridge.
Several nondestructive evaluation (NDE) technologies were studied to determine their efficacy as scanning devices to detect internal moisture and artificial decay pockets. Large bridge-sized test specimens, including sawn timber and glued-laminated timber members, were fabricated with various internal defects. NDE Technologies evaluated in this research were ground penetrating radar (GPR), microwave scanning, ultrasonic pulse velocity, ultrasonic shear wave tomography, and impact echo methods. Each NDE technology was used to evaluate a set of seven test specimens over a 2-day period and then raw data scans were processed into two-dimensional, internal defect maps. Several parameters were, compared including the relative size, orientation, and moisture conditions of the internal defect. GPR was the most promising NDE technology and is currently being more rigorously evaluated within the laboratory. The study results will be useful in the further development of a reliable NDE scanning technique that can be utilized to inspect the primary structural components in historic covered timber bridges.
Bridge inspection using a drone, also referred to as an unpiloted aircraft system, has gained more interest in recent years among bridge owners, researchers, and stakeholders because of its efficiency and effectiveness. In fact, numerous bridges classified as structurally deficient in the United States that require more attention and effort for maintenance activities can be inspected using drones in an efficient manner. The primary goal of this project was to evaluate drones as supplemental bridge inspection tools for bridges that present accessibility challenges for inspectors. To accomplish this goal, an extensive literature review and technical survey were initially conducted to gain knowledge of the state-of-the-art and practices and critical considerations that should be accounted for while conducting inspections. Also, analysis of the drones was conducted and the most suitable drone for bridge inspections was selected. To recognize the drone-enabled inspection efficiency, preliminary inspections were conducted for structural damage identification in three structures, including a reinforced masonry building and two pedestrian timber deck bridges. With the knowledge and techniques established during the preliminary inspections, a six-stage recommended bridge inspection protocol using the drone was proposed and applied to two in-service highway timber bridges, including a timber arch bridge and a three-span timber girder bridge in South Dakota. Through the acquisition and analysis of image and video data, the effectiveness of the drone platform was evaluated in terms of image quality, damage identification and quantification, and comparisons with results from traditional inspections conducted on the bridges. This study details drone-enabled inspection advantages and challenges and provides conclusions and recommendations for future work. A key finding demonstrated throughout this project was that different types of structural damage on the bridges were efficiently identified using the drone.
International Nondestructive Testing and Evaluation of Wood Symposium
In this report, wooden members of sizes typically used in bridge construction are examined using x-ray computerized tomography (CT) to determine the presence of internal decay. This report is part of an overall study in which Douglas-fir (Pseudotsuga menziesii) glue-laminated (glulam) beams and solid sawn timbers were inoculated with brown rot fungus, Fomitopsis pinicola, and exposed to aboveground conditions approximately 25 miles (40 km) north of Gulfport, Mississippi, USA. The goal of the overall study is to develop interior decay within the test specimens and then identify and characterize the decay using a variety of nondestructive testing (NDT) techniques. One NDT technique used is x-ray CT. The pixel brightness (PB) of CT scan images is proportional to the specific gravity (SG) at that location; high SG materials appear brighter whereas low SG materials appear darker. The consumption of wood by fungus decreases the wood SG; however, fungal progression takes place in areas where sufficient moisture is present. The presence of moisture increases wood SG as detected by the CT scan, which masks the effect of the fungal decay, which is a common co-occurrence with many NDT techniques. To identify incipient decay, it is necessary to examine the ring structure both within and outside of the area of moisture. Quantifying the extent of the decay requires correlating the PB to known SG values for both dry wood and wood of varying moisture content. In this report, the relationship between wood SG, moisture content, and PB was quantified.
This research aims to develop a new bridge inspection approach using unmanned aerial vehicle (UAV) coupled with digital image correlation (DIC) system. The DIC system incorporating UAV images can measure displacements or strains by analyzing patterns of reference and deformed images. As part of this research, a commercially available UAV, DJI Matrice 210, was integrated with the DIC system using a 3D printed mounting plate, and the joint UAV-DIC system was utilized to inspect a timber bridge girder in the Structure Lab. Then, the UAV-DIC system inspected an existing timber slab bridge in Pipestone, Minnesota, but the system was not able to efficiently identify critical damage due to its instability caused by windy conditions. Therefore, only the UAV equipped with a gimbal camera was operated to perform the bridge inspection. A significant number of images from the UAV were used and analyzed through a conventional image analysis algorithm within ImageJ software for damage quantification. The major conclusion from this research was that the UAV-DIC system was only able to detect and quantify damage (i.e., crack) on the considered girder under almost zero ambient wind conditions, and the UAV integrated with the image analysis algorithm was capable of damage identification and quantification for the inspected bridge.