Many uncertainties remain in tunnel health evaluation due to a lack of specific information, data scarcity, misleading or conflicting information due to the complex nature of geo‐materials, and even the ambiguity in the concept of tunnel health. This article addresses the fuzzy analytic hierarchy process (AHP) synthetic evaluation models, which merge different types of data from multiple sensors to map them into the health rating scores of shield tunnels. A piecewise distribution was chosen for membership functions, and an exponential scale was introduced for a better characterization of the scales for weight sets. A series of fuzzy operation symbols, namely fuzzy operators, were defined to yield the fuzzy synthetic evaluation indexes (FSEIs) for monitoring factors. The fuzzy‐AHP evaluation procedure applied to the models was demonstrated. Moreover, a case study on Nanjing Yangtze River Tunnel was presented to verify the feasibility and efficiency of the models and the procedure. The fuzzy‐AHP health evaluations for monitoring factors, segments, rings, and the whole tunnel were implemented in succession using the models and following the procedure. The calculated FSEIs were compared with the rating scales to determine the corresponding action strategies. The segments with poor health conditions can then be identified for administrative maintenance or repairing measures. Such evaluation results will enhance the knowledge of designers and aid them for optimization when they are designing similar tunnels. The investigations indicate that the proposed fuzzy‐AHP models characterize the fuzziness of tunnel health well and will be useful for clarifying the tunnel health evaluation uncertainties to both designers and administrators.
With the continuous development of my country’s economy, the demand for various high-tech products has also increased. Therefore, as an important detection tool for industrial production, optical fiber sensors have become one of the important tools for the development of the Internet of Things industry. Real-time monitoring and diagnosis of structural performance of main engineering structures, timely perception of structural damage, safety evaluation, structural performance changes and remaining life predictions Make maintenance decisions to improve, improve and ensure the operational efficiency of engineering structures and protect people’s lives and property. The structural health monitoring system collects data about the status of structural services in real time, uses specific damage recognition algorithms to determine the location and scope of damage, evaluates structural safety in a timely and effective manner, predicts structural performance changes, and provides early warning for emergencies. In recent years, structural health monitoring systems have become one of the hot topics in international academic research. In response to the needs of large-scale civil engineering health monitoring systems at home and abroad for monitoring methods, a method for structural health monitoring of high-piled wharves based on optical fiber sensor technology is studied. The structural health monitoring system of the high-piled wharf includes an intelligent monitoring system and a signal demodulation system.
The subway train-induced structural vibration and ambient noise may cause annoyance and other negative influences on the human body. Presently, limited models have been developed to execute the quantitative evaluation of the combined annoyance caused by both structural vibration and ambient noise. In this study, a fuzzy membership function and normal distribution function were coupled to describe the fuzziness and randomness of human annoyance responses; a novel annoyance evaluation model was proposed to assess the structural vibration and ambient noise; and the annoyance of human was classified into six grades. Subsequently, we integrated an actual case into this study to calculate and analyze the combined annoyance degree. The applied results were compared with the standard limits, in which the rationality and superiority of the proposed model were verified. The results exhibit the notion that the proposed models perform well and can serve as a reference for spatial planning and development in the nearby subway environment.
Many monitoring indexes affect the health condition of foundation pits to different extents. How to use a massive on-site monitoring dataset to quantitatively assess the health of foundation pits is a problem that deserves due consideration. This paper proposes a foundation pit health assessment model based on the fuzzy analytical hierarchy process (AHP) method. First, factors affecting the health of foundation pits are classified by the AHP, a hierarchical factor system for foundation pit health assessment is established, and the index scale method is used to assign weights to assessment factors at different hierarchical levels. Combined with the fuzzy mathematical method, the membership functions of four health degree levels (A to D) are constructed, and the determination range of each health degree level is given to realize the quantitative calculation of the health condition from the bottom-level assessment factor to the overall foundation pit. Considering that each assessment factor involves many monitoring points and different monitoring data, a comprehensive assessment operator is also constructed to highlight the most adverse impact. Finally, the proposed model is used to perform a health assessment of an actual foundation pit project, and the variation in the foundation pit health during the entire monitoring period is obtained. The health grade of the foundation pit is determined to be B, which is a basically healthy condition consistent with the on-site inspection results.
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