This paper reports the results of an experimental program to investigate the bonding behavior of two different types of fiber-reinforced polymer (FRP) systems for strengthening RC members: externally bonded carbon (EBR) plates and bars or strips externally applied with the near-surface-mounted (NSM) technique. The overall experimental program consisted of 18 bond tests on concrete specimens strengthened with EBR carbon plates and 24 bond tests on concrete specimens strengthened with NSM systems (carbon, basalt, and glass bars, and carbon strips). Single shear tests (SST) were carried out on concrete prisms with low compressive strengths to investigate the bonding behavior of existing RC structures strengthened with different types of FRP systems. The performance of each reinforcement system is presented, discussed, and compared in terms of failure mode, debonding load, load-slip relationship, and strain distribution. The findings indicate that the NSM technique could represent a sound alternative to EBR systems because it allows debonding to be delayed, and hence FRP tensile strength to be better exploited.

In recent years, Artificial Neural Networks (ANNs) have been introduced in Structural Health Monitoring (SHM) systems. A semi-supervised method with a data-driven approach allows the ANN training on data acquired from an undamaged structural condition to detect structural damages. In standard approaches, after the training stage, a decision rule is manually defined to detect anomalous data. However, this process could be made automatic using machine learning methods. This paper proposes a semi-supervised method with a data-driven approach to detect structural anomalies. The methodology consists of: 1) a Variational Autoencoder (VAE) to approximate undamaged data distribution and 2) a One-Class Support Vector Machine (OC-SVM) to discriminate different health conditions using damage-sensitive features extracted from VAE’s signal reconstruction. The method is applied to a scale steel structure that was tested in nine damage scenarios by IASC-ASCE Structural Health Monitoring Task Group.

Intuitively, the fire endurance of concrete members reinforced with fiber reinforced polymer (FRP) bars is related to the decrease in the mechanical properties of the materials concerned, especially resin. Large-scale fire tests recently performed on nine concrete slabs reinforced with glass FRP bars demonstrated the importance of bond between FRP and concrete for performance under fire situations. The experimental results showed that (1) the length of the FRP bars in the zone of slabs not directly exposed to fire (namely, anchoring length in fire situations) can be much more relevant to fire endurance than the concrete cover in the zone directly exposed to fire; and (2) the shape of the bar, for instance bent at the end, allows a reduction in anchoring length. From a design point of view, evaluating the necessary anchoring length through a bond model seems to be a key aspect. Full-scale test results, extensively presented elsewhere, are used in this paper to investigate the bond behavior of FRP bars embedded in concrete at high temperature and to assess a procedure to predict bond stress, slip, and load transfer at elevated temperature, based on both the results of numerical thermal analysis and the predictions of a bond theoretical model adjusted for fire situations. The design procedure outlined for calculating the minimal required anchoring length proves a valuable approach for the practicing engineer and stands together with the experimental and numerical results presented earlier. Finally, design nomograms are shown as examples of application of the procedure.

Despite the extensive research on the debonding behavior of fiber-reinforced polymer (FRP) strengthening systems, no standard methodology has been established on its experimental characterization. To assess the performance and reliability of small-scale testing on near-surface mounted (NSM) FRP strengthening systems, an experimental program was carried out on a series of nine NSM FRP strengthening systems (bars and strips), in the framework of an international round robin testing (RRT). Eleven laboratories and seven manufacturers and suppliers participated in this extensive international exercise, which regarded both NSM and EBR FRP strengthening systems. The experimental results are discussed and compared in this paper in order to investigate (1) the bond behavior of NSM strips and bars, (2) the influence of alternative single-bond or double-bond shear pulling setups adopted by the participating laboratories, and (3) the influence in concrete strength due to different constituent materials between countries in applying a prescribed mix. The results show no significant influence of the concrete strength on both failure modes and loads. On the other hand, axial stiffness and surface treatment of the FRP NSM systems strongly affect both the bond strength and the parameters identifying the bond laws. Differences of bond strength experimentally recorded for the same FRP NSM systems tested according to different setups range between 10 and 25%. Therefore, for standard bond test protocol, the single shear bond test setup is more reliable, due to its higher feasibility compared with the double shear test setup.

The effectiveness of externally bonded fiber-reinforced polymer (FRP) laminates for strengthening existing reinforced concrete (RC) structures is strictly related to the bond of the FRP reinforcement to the concrete substrate. Although numerous experimental studies have investigated this bond, experimental data concerning cyclic tests on both FRP sheets and plates applied on concrete specimens are still lacking. Thus, a series of single shear tests (SSTs) under both monotonic and cyclic actions, without inversion of sign, were performed on concrete prismatic specimens reinforced with carbon FRP (CFRP) sheets or plates. To evaluate and compare the influence of cyclic external actions on the bonding behavior of sheets and plate reinforcements, the results provided by monotonic and cyclic tests are reported and discussed in this paper. In particular, force-displacement relationships, axial strains, and shear stresses recorded along the FRP reinforcement are reported; the influence of the load path on the FRP...

The failure of non-structural components after an earthquake is among the most expensive earthquake-incurred damage, and may also have life-threatening consequences, especially in public buildings with very crowded facilities, because exposition is high and the risk increases accordingly. The assessment of existing non-structural components is particularly complex because in-depth in situ investigation is necessary to detect the presence of deficiencies or damage. This problem concerns interior and exterior partitions made of various materials (e.g., glass and masonry), as well as equipment and facilities in construction (building, industry, and infrastructure). Defining the boundary conditions of these components is of paramount importance. Indeed, external restraints (i) affect dynamic properties and, thus, the action experienced during an earthquake, and (ii) influence the capacity to detach the component before failure from the bearing structure (e.g., an infill wall connected to the main structural frame, or equipment connected to secondary structural members such as floors). The authors, therefore, conducted environmental vibration tests of an infill wall and refined a finite element model to simulate typical damage scenarios to be implemented on the wall. Selected damage scenarios were then artificially realized on the existing infill and further ambient vibration tests were performed to measure the accelerations for each of them. Finally, the authors used these accelerations to detect the damage by means of established OMA, as well as innovative machine learning techniques. The results showed that convolutional variational autoencoders (CVAE), coupled with a one-class support vector machine (OC-SVM), identified the anomaly even when the OMA exhibited limited effectiveness. Moreover, the machine learning procedure minimizes human interaction during the damage detection process.