Replacement of Flexural and Shear Reinforcement of Double-Column Bridge Bent with CFRP under Combined loadings

The bridge bent is the most critical structural component of short span bridge that highly affected by different types of loadings. The bent failure has been observed due to in plane and out of plane loadings. Strengthening techniques are utilized for existing bridges. However, a replacement technique can be used for the new bridges to avoid bent failure. Moreover, the effect of combined loading on bent performance need to be evaluated. Therefore, this study assessed the performance of bridge bent under in plane, out of plane and combined loadings. Furthermore, replace the traditional flexural and shear steel reinforcement of the columns with CFRP bars. The performance of bent is assessed numerically by finite element analysis. For this purpose, six numerical bent models are developed. The first three models with traditional steel bars and the remaining models with CFRP rebars. The results demonstrated that out of plane loadings has more impact on the bent structural performance than other loading cases. Flexural and shear failures are observed in the columns for models with steel rebars. The failure started from lower side of the column for both in plane and out of plane loadings showing low resistance. The steel rebars yielded in early stage of loading indicating limited stiffness. However, the bent performance has been enhanced by replacing rebars with CFRP. The bent stiffness has slightly improved by replacing with less diameter of CFRP rods and stirrups. In addition, the CFRP bars showed considerable resistance and hardly showed plasticity during apply loading indicating that the CFRP is suitable material to replace steel reinforcement.

Mechanical and Electrical Characteristics of Lightweight Aggregate Concrete Reinforced with Steel Fibers

There is increased interest in applying electromagnetic (EM) shielding to prevent EM interference, which destroys electronic circuits. The EM shielding’s performance is closely related to the electrical conductivity and can be improved by incorporating conductive materials. The weight of a structure can be reduced by incorporating lightweight aggregates and replacing the steel rebars with CFRP rebars. In this study, the effects of lightweight coarse aggregate and CFRP rebars on the mechanical and electrical characteristics of concrete were investigated, considering the steel fibers’ incorporation. The lightweight coarse aggregates decreased the density and strength of concrete and increased the electrical conductivity of the concrete, owing to its metallic contents. The steel fibers further increased the electrical conductivity of the lightweight aggregate concrete. These components improved the EM shielding performance, and the steel fibers showed the best performance by increasing shielding effectiveness by at least 23 dB. The CFRP rebars behaved similarly to steel rebars because of their carbon fiber content. When no steel fiber was mixed, the shielding effectiveness increased by approximately 2.8 times with reduced spacing of CFRP rebars. This study demonstrates that lightweight aggregate concrete reinforced with steel fibers exhibits superior mechanical and electrical characteristics for concrete and construction industries.

Condition assessment of reinforced concrete systems with fusion-bonded epoxy-coated rebars

Corrosion assessment of reinforced concrete (RC) structures with fusion-bonded-epoxy (FBE) coated steel rebars is a challenge because the common inspection methods and data cannot be applied or interpreted in the same way as that for the systems with uncoated rebars. If corrosion detection tools based on techniques such as half-cell potential (HCP), linear polarization resistance (LPR), or electrochemical impedance spectroscopy (EIS) are used for the assessment of systems with FBE coated steel rebars without considering the difference in the electrochemical conditions between coated and uncoated systems, then, the interpretation can result in the inability to detect ongoing corrosion. Therefore, the objective of this paper is to examine the suitability of these inspection methods and data to be applied to the RC systems with FBE coated steel rebars. For this, the suitability of test methods on HCP, LPR, and EIS for assessing corrosion conditions of RC structures was assessed using laboratory specimens and field structures. Field investigation using HCP shows that the HCP could not detect corrosion of FBE rebars unless the coating was severely disbonded due to corrosion of steel rebars. Also, the suitability of test methods based on HCP, LPR, and EIS was assessed by additional laboratory specimens. Although complex, only the EIS technique could reliably detect the corrosion conditions of the FBE coated steel rebars embedded in concrete. Therefore, a way forward to assess RC structures using EIS technique is proposed.

Experimental Evaluation of the Bending Behavior of a Drilled Shaft with Partial Casing under Lateral Loads

Few studies, especially those related to field tests, have examined the bending behaviors of drilled shafts with partial casings (DSPCs). This work reports the results of experimental studies on the behavior of DSPCs under lateral loads, including an in situ test and a set of laboratory tests. First, a DSPC with a diameter of 2 m and length of 87.9 m was studied in clay beds, and a steel casing with a diameter of 2.0 m and length of 33 m was used. In this test, strain gauges were distributed along the steel rebars in the concrete pile and the wall of the steel tube at different depths, and thus the longitudinal strains of the concrete pile and the steel tube could be studied. Second, laboratory experiments were implemented with reinforced concrete-filled steel tubular columns under pure bending conditions. In these tests, strain gauges were distributed along the steel rebars in the concrete pile and the walls of the steel tubes at the pure bending section of the specimens. Different wall thicknesses and drilling fluid conditions were considered. The field test results show that the strain of the concrete piles and the steel tubes were linearly distributed at the same cross-section. This means that a DSPC remains a flat plane after it deforms. Whereas a correction coefficient related to the loading level need to be considered in the calculation of the bending stiffness. Laboratory studies show that the strain of DSPCs was linearly distributed at a small bending moment under the best bond-quality condition, whereas obvious nonlinear behaviors were shown under a large bending moment with poor bond-quality conditions.

An Image-Based Steel Rebar Size Estimation and Counting Method Using a Convolutional Neural Network Combined with Homography

Conventionally, the number of steel rebars at construction sites is manually counted by workers. However, this practice gives rise to several problems: it is slow, human-resource-intensive, time-consuming, error-prone, and not very accurate. Consequently, a new method of quickly and accurately counting steel rebars with a minimal number of workers needs to be developed to enhance work efficiency and reduce labor costs at construction sites. In this study, the authors developed an automated system to estimate the size and count the number of steel rebars in bale packing using computer vision techniques based on a convolutional neural network (CNN). A dataset containing 622 images of rebars with a total of 186,522 rebar cross sections and 409 poly tags was established for segmentation rebars and poly tags in images. The images were collected in a full HD resolution of 1920×1080 pixels and then center-cropped to 512 × 512 pixels. Moreover, data augmentation was carried out to create 4668 images for the training dataset. Based on the training dataset, YOLACT-based steel bar size estimation and a counting model with a Box and Mask of over 30 mAP was generated to satisfy the aim of this study. The proposed method, which is a CNN model combined with homography, can estimate the size and count the number of steel rebars in an image quickly and accurately, and the developed method can be applied to real construction sites to efficiently manage the stock of steel rebars.