To investigate anchorage performance in a multidirectional arrangement of reinforcement bars, simulations are carried out using a three-dimensional discrete model, specifically the three-dimensional rigid-body-spring model (3D RBSM). In 3D RBSM, a material is partitioned into an assemblage of rigid bodies interconnected along their contact boundaries through discrete springs. In this study, for example, RC is meshed into rigid bodies with a size of 1–2 cm, where the opening, closing, and sliding of cracks can be modeled by the deformation or transmission of internal forces between two rigid bodies through the use of nonlinear springs along their contact boundaries. Simulations are conducted, one with the number of transverse reinforcement bars as a parameter and another varying the clear space between column reinforcement and embedded reinforcement in a congested joint. The results of these simulations are evaluated through comparison with experimental data; good agreement is observed with respect to anchorage capacity, crack pattern, and failure mode. The simulation results indicate the concrete strength is reduced if the reinforcement spacing between the column reinforcement and the embedded reinforcement is very close because of a nonhomogeneous behavior of concrete, where in the previous experiment large voids around reinforcing bars were observed. Simulations also suggest that the propagation of local cracks depends on the arrangement of reinforcing bars. This research shows how mesoscale analysis using the 3D RBSM can be a useful technique to consider the effect of a multidirectional arrangement of reinforcing steel that results in a complex stress and strain state due to the 3D bond transfer mechanism. It can be used for the design of a reinforcement arrangement in congested areas that is not addressed by the design code. As a result, a rational reinforcement arrangement can be designed on a case-by-case basis.

Monitoring damage in concrete structures is crucial for maintaining the health of structural systems. The implementation of computer vision has been the key for providing accurate and quantitative monitoring. Recent development uses the robustness of deep-learning-aided computer vision, especially the convolutional neural network model. The convolutional neural network is not only accurate but also flexible in various scenarios. The convolutional neural network has been constructed to classify image in terms of individual pixel, namely pixel-level detection, which is especially useful in detecting and classifying damage in fine-grained detail. Moreover, in the real-world scenario, the scenes are mostly very complex with varying foreign objects other than concrete. Therefore, this study will focus on implementing a pixel-level convolutional neural network for concrete surface damage detection with complicated surrounding image settings. Since there are multiple types of damage on concrete surfaces, the convolutional neural network model will be trained to detect three types of damages, namely cracks, spallings, and voids. The training architecture will adopt U-Net and DeepLabV3+. Both models are compared using the evaluation metrics and the predicted results. The dataset used for the neural network training is self-built and contains multiple concrete damages and complex foregrounds on every image. To deal with overfitting, the dataset is augmented, and the models are regularized using L1 and Spatial dropout. U-Net slightly outperforms DeepLabV3+ with U-Net scores 0.7199 and 0.5993 on F1 and mIoU, respectively, while DeepLabV3+ scores 0.6478 and 0.5174 on F1 and mIoU, respectively. Given the complexity of the dataset and extensive image labeling, the neural network models achieved satisfactory results.

Nowadays, seismic design code in Japan is becoming more stringent. To satisfy the strict requirement, larger numbers of reinforcements must be placed, resulting in increased reinforcement congestion. To reduce the reinforcement congestion, mechanical anchorage is becoming popular in use instead of conventional hook rebar. However the behavior of mechanical anchorage placed in thin cover zone is not well understood, and the use of this is still limited. In this study, the discrete element method was used to investigate the performance of mechanical anchorage placed in thin cover depth zone, and the pull-out test in previous research was simulated. To assume that anchor plates welded to bar end are difficult to place side by side due to closely-spaced reinforcements, the position of anchor plate was taken as main parameter. The result shows that the anchorage performance changed according to the transverse bars effect and the shift of the mechanical anchorage. The simulations show good agreement with experiment data in terms of the anchorage capacity, crack pattern and failure mode.

When mechanical anchorages are placed near the surface of a beam-column knee joint, anchorage failure may occur because of local stresses from anchorage plates. Perpendicular beams placed on both sides of the joint can be a way to avoid the occurrence of this failure. In this study, a meso-scale discrete analysis using 3D RBSM is conducted to investigate the effect of perpendicular beams on the behavior of a beamcolumn knee joint with mechanical anchorages and their effect on failure mode. By studying the internalstresses and cracks in a beam-column knee joint with mechanical anchorages, perpendicular beams can improve its performance in terms of loading capacity and failure behavior. Perpendicular beams are known to have two effects. The perpendicular beams increase bond performance along the development length of anchorages and resist the opening of the diagonal cracks. It has the same failure mode as in the case without perpendicular beams which is an anchorage failure, but the occurrence of the anchorage failure is delayed in this case. It is also inferred by simulation that the effective length of the perpendicular beams is determined by approximately 45 degrees inclination to the longitudinal beam, measured from the original position of anchorage plates.