Bus Rapid Transit (BRT) has become an increasing source of concern for public transportation of modern cities. Traditional contact sensing techniques during the process of health monitoring of BRT viaducts cannot overcome the deficiency that the normal free-flow of traffic would be blocked. Advances in computer vision technology provide a new line of thought for solving this problem. In this study, a high-speed target-free vision-based sensor is proposed to measure the vibration of structures without interrupting traffic. An improved keypoints matching algorithm based on consensus-based matching and tracking (CMT) object tracking algorithm is adopted and further developed together with oriented brief (ORB) keypoints detection algorithm for practicable and effective tracking of objects. Moreover, by synthesizing the existing scaling factor calculation methods, more rational approaches to reducing errors are implemented. The performance of the vision-based sensor is evaluated through a series of laboratory tests. Experimental tests with different target types, frequencies, amplitudes and motion patterns are conducted. The performance of the method is satisfactory, which indicates that the vision sensor can extract accurate structure vibration signals by tracking either artificial or natural targets. Field tests further demonstrate that the vision sensor is both practicable and reliable.

No abstract

The paper presents an intelligent real-time slope surface deformation monitoring system based on binocular stereo-vision. To adapt the system to field slope monitoring, a design scheme of concentric marking point is proposed. Techniques including Zernike moment edge extraction, the least squares method, and k-means clustering are used to design a sub-pixel precision localization method for marker images. This study is mostly focused on the tracking accuracy of objects in multi-frame images obtained from a binocular camera. For this purpose, the Upsampled Cross Correlation (UCC) sub-pixel template matching technique is employed to improve the spatial-temporal contextual (STC) target-tracking algorithm. As a result, the tracking accuracy is improved to the sub-pixel level while keeping the STC tracking algorithm at high speed. The performance of the proposed vision monitoring system has been well verified through laboratory tests.

Given the flammability of power cables and the high cost of utility tunnel construction, power cable fires cause serious economic losses and are associated with a negative social impact. In the study, a weighted fuzzy Petri net and an event tree are combined to propose a quantitative evaluation method to mitigate cable fire risks in a utility tunnel. First, cable fire risk factors are analyzed. Given the lack of utility tunnel cable fire historical data, fuzzy theory is used to calculate the failure probability of the primary event. Second, a weighted fuzzy Petri net is used for fuzzy reasoning, and an event tree is used to analyze all possible consequences. Subsequently, the numerical simulation method is used to quantify the loss from the cable fire and thereby quantify the risk of cable fire. Finally, the effect of different risk factors on a cable fire is analyzed to determine the main factors that affect cable fires. Simultaneously, the control ability of different control measures with respect to the fire is analyzed to determine key control measures. A case study of a utility tunnel cable cabin in Liupanshui in Guizhou is employed to validate the utility of the proposed method.

With the large-scale construction of urban rail transit, it will lead to the intersection and transfer of various lines, resulting in more transfer stations. The transfer station is a collection point for multiple subway lines, which is difficult to construct and has a high construction risk. The construction of the new subway station and the operation of the existing subway station are mutually influenced during the close-attached undercrossing construction. Considering the two objectives of ensuring the smooth operation of the existing subway station and the safe construction of the new subway station, this paper comprehensively analyzes the possible safety risk factors during the construction of the new subway station close-attached undercrossing the existing operating station and identifies 75 preliminary risk factors by means of literature review and on-site investigation. Then the Delphi Method and Entropy Weight Method are used to screen the preliminary risk factors, and the main risk factors with greater influence are retained, so that 49 key risk factors are obtained. According to the list of key risk factors, a safety risk assessment index system including 2 first-level indexes, 12 second-level indexes, and 49 third-level indexes is established. Based on the index system, this paper establishes a safety risk assessment model by using Analytic Hierarchy Process (AHP) and Fuzzy Matter Element Method (FMEM). The model first calculates the weight of each index by using AHP, calculates the comprehensive correlation degree of each index by using FMEM, classifies the risk grade of each index according to the comprehensive correlation degree, and determines the risk grade of the project. Finally, the safety risk assessment model is applied to the Dongdalu Station project of Chengdu Rail Transit Line 8. The result shows that the risk grade of this project is moderate risk, which is basically consistent with the actual situation, indicating that the model has good practicability. In this paper, a new safety risk assessment model for subway close-attached undercrossing construction is proposed, which fills the gap in the field of safety risk assessment for the construction of the new subway station close-attached undercrossing the existing operating station.

No abstract

No abstract

No abstract