Search

The rebar installation quality significantly impacts the safety and durability of reinforced concrete (RC) structures. Traditional manual inspection is time-consuming, inefficient, and highly subjective. In order to solve this problem, this study uses a depth camera and aims to develop an intelligent inspection method for the rebar installation quality of an RC slab. The Random Sample Consensus (RANSAC) method is used to extract point cloud data for the bottom formwork, the upper and lower rebar lattices, and individual rebars. These data are utilized to measure the concrete cover thickness, the distance between the upper and lower rebar lattices, and the spacing between rebars in the RC slab. This paper introduces the concept of the "diameter calculation region" and combines point cloud semantic information with rebar segmentation mask information through the relationship between pixel coordinates and camera coordinates to measure the nominal diameter of the rebar. The verification results indicate that the maximum deviations for the concrete cover thickness, the distance between the upper and lower rebar lattices, and the spacing of the double-layer bidirectional rebar in the RC slab are 0.41 mm, 1.32 mm, and 5 mm, respectively. The accuracy of the nominal rebar diameter measurement reaches 98.4%, demonstrating high precision and applicability for quality inspection during the actual construction stage. Overall, this study integrates computer vision into traditional civil engineering research, utilizing depth cameras to acquire point cloud data and color results. It replaces inefficient manual inspection methods with an intelligent and efficient approach, addressing the challenge of detecting double-layer reinforcement. This has significant implications for practical engineering applications and the development of intelligent engineering monitoring systems. [ABSTRACT FROM AUTHOR]

This paper presents an actuated spring-loaded inverted pendulum model with a vertically constrained suspended load mass to predict the vertical GRF and energetics of walking and running. Experiments were performed to validate the model prediction accuracy of vertical GRF. The average correlation coefficient was greater than 0.97 during walking and 0.98 during running. The model's predictions of energy cost reduction were compared with experimental data from the literature, and the difference between the experimental and predicted results was less than 7%. The predicted results of characteristic forces and energy cost under different suspension stiffness and damping conditions showed a tradeoff when selecting the suspension parameters of elastically suspended backpacks. [ABSTRACT FROM AUTHOR]

Backpacks are essential for travel but carrying a load during a long journey can easily cause muscle fatigue and joint injuries. Previous studies have suggested that suspended backpacks can effectively reduce the energy cost while carrying loads. Researchers have found that adjusting the stiffness of a suspended backpack can optimize its performance. Therefore, this paper proposes a stiffness-adjustable suspended backpack; the system stiffness can be adjusted to suitable values at different speeds. The stiffness of the suspended backpack with a 5-kg load was designed to be 690 N/m for a speed of 4.5 km/h, and it was adjusted to 870 and 1050 N/m at speeds of 5.5 and 6.5 km/h, respectively. The goal of this study was to determine how carrying a stiffness-adjustable suspended backpack affected performance while carrying a load. Six healthy participants participated in experiments where they wore two backpacks under three conditions: the adjustable-stiffness suspended backpack condition (S_A), the unadjustable-stiffness suspended backpack condition (S_UA), and the ordinary backpack condition (ORB). Our results showed that the peak accelerations, muscle activities, and peak ground reaction forces in the S_A condition were reduced effectively by adjusting the stiffness to adapt to different walking speeds; this adjustment decreased the metabolic cost by 4.21 ± 1.21% and 2.68 ± 0.88% at 5.5 km/h and 4.27 ± 1.35% and 3.38 ± 1.31% at 6.5 km/h compared to the ORB and S_UA, respectively., (Copyright © 2022 by ASME.)