This paper presents an initial experimental result concerning the behavior of near-surface mounted (NSM) carbon fiber reinforced polymer (CFRP) strips embedded in a concrete substrate at elevated temperatures. Thermal stresses varying from 25°C [77°F] to 200°C [392°F] are applied for three hours. The experimental program is comprised of 48 CFRP-concrete specimens bonded with an ordinary or high-temperature epoxy adhesive and their comparative performance is of interest in the present investigation. Emphasis is placed on the residual capacity of the conditioned NSM CFRP-concrete interface and corresponding failure mode. Test results show that the interfacial strength of the specimens bonded with the ordinary epoxy is maintained until 75°C [167°F] is reached, while the strength noticeably decreases with an increasing temperature above this limit. The specimens with the high-temperature epoxy preserve interfacial capacity up to 200°C [392°F] despite a trend of strength-decrease being observed. The failure of the test specimens is brittle irrespective of adhesive type. Interfacial damage is localized along the bond-line with the presence of hairline cracks that further develop when interfacial failure is imminent.

The purpose of this study is to facilitate damage detection and health monitoring in concrete bridge girders without the need for visual inspection while minimizing field measurements. Simple span beams with different geometry, material and cracking parameters were modeled using Abaqus finite element analysis software to obtain stiffness values at specified nodes. The resulting databases were used to train two Artificial Neural Networks (ANNs). The first network (ANN1) solves the forward problem of providing a health index parameter based on predicted stiffness values. The second network (ANN2) solves the inverse problem of predicting the most probable cracking pattern. For the forward problem, ANN1 had the geometric, material and cracking parameters as inputs and stiffness values as outputs. This network provided excellent prediction accuracy measures (R2 > 99%). ANN2 had the geometric and material parameters as well as stiffness values as inputs and cracking parameters as outputs. This network provided less accurate predictions compared to ANN1, however, ANN2 results were reasonable considering the non-uniqueness of this problem's solution. An experimental verification program will be conducted to qualify the effectiveness of the method proposed. This test program is described in details in the present paper.

The most advanced method of investigating the performance of a structure is to continuously track the strain, deflection, and acceleration by analysing data collected from a series of wireless sensors installed on the structural member. Before analysing the data, it is important to assure the reliability of the data by verifying that all sensors are working properly. For an instance, in the reinforced concrete structure sensors are attached to the reinforcement bars and might be destroyed while pouring the concrete. Besides, sensors might malfunction due to excessive variation of temperature, load, or any other causes. Data-driven and structural models-based are two damage detection techniques in civil structures. In this study, the data driven method, a direct approach to damage assessment, was followed; this approach does not require structural modeling, such as finite element analysis. In this method, the existence of damage and its location are interpreted by pattern matching of the data series at different time ranges. The objective of this study was to develop new techniques to detect defective sensors based on the pattern matching method that included the Auto Regression Xeogeneous model. As a case study, Portage Creek Bridge was selected, located in British Colombia, Canada. Data sets of strain and temperature gages were downloaded from a database connected to the instrumented pier of the bridge and filtered and normalized continuously. The condition of a set of sensors installed in the pier was determined, using a method developed based on the concept of the sequential and binary search techniques. Using sensitivity analyses of the developed models, defective sensors were detected by pattern matching of simulated and measured or real data.

Fiber reinforced polymer (FRP) composites are now common structural materials for both new construction and repair/rehabilitation of existing structures. Since the 1980s researchers have developed a significant body of knowledge on externally-bonded composites for infrastructure repair; however, with emphasis on the use of epoxy systems (matrix and adhesives). Externally-bonded FRP composites with polyurethane matrices and adhesives have recently been investigated due to advantages in constructability and mechanical properties. However, little research is available on bond of polyurethane composites to concrete infrastructure, and direct comparisons between performance of epoxy and polyurethane systems. This paper presents several small-scale experiments to characterize the mechanical properties of the bond to concrete of polyurethane FRP composites alongside with epoxy composites. The tests include 3-point bending tests of concrete beams reinforced with the composite materials, lap shear tests, and coupon tensile tests. Strain data collected from the lap shear experiments were used to develop bond-slip relationships of the composite materials that were then implemented in a finite element model and compared with the experimental flexural results. While polyurethane matrices and adhesives are typically characterized by lower shear and normal strengths, results demonstrate the flexibility of the polyurethane matrix proved advantageous in spreading the bond stresses over a larger area compared with epoxy composites. Therefore polyurethane-reinforced concrete beam stiffness and strength properties are comparable with the epoxy counterparts.

Recently, several infrastructure failures have highlighted the importance of structural inspection and increase awareness of the need for efficient structural health monitoring and damage detection techniques. The Development of non-contact monitoring technique that is efficient and requires little preparation to implement would greatly benefit the civil engineering and construction community. Close range photogrammetry is a non-contact measurement technique that can be used to monitor a specimen’s deformation as it undergoes loading. This research investigates utilising an image matching algorithm to measure the deflection profile of concrete beams. The present paper illustrates the efficiency of the image matching algorithm (digital image correlation) in measuring the full deflection profile along a concrete beam. Five reinforced concrete beams, 2400 mm (94.48 in.) long, 250 mm (10 in.) deep and 150 mm (6 in.) wide were tested under 4-point bending. Three different surface treatment configurations for the test specimen’s side faces as well as two types of longitudinal flexural reinforcement, steel and CFRP, were used. Two LVDTs were used to measure the deflection to validate the proposed digital image correlation algorithm. It was concluded that the image matching algorithm can be used efficiently to measure deflection profile of a flexural member. Despite all existing health monitoring techniques, image matching has the potential to reconstruct the deflection profile along the whole member length to evaluate its current structural behaviour.