A Biological Structure-Inspired Infrastructure Health Assessment Method

Meng Zhang, Gang Li, Bin He, Dongming Zhang, Yu Tian, Bin Cheng

Abstract: Structural health monitoring with wireless sensor networks(WSNs) plays an increasingly critical role in modern municipal. While there still remains a gap in getting a comprehensive understanding of complex structural data. Skin diseases can be well diagnosed with modern medical technology, from which similar methods can be learned to improve the intuitiveness and accuracy of structural health monitoring. This work proposes a multi-layered skin-like architecture based method(MSHA), which each layer has its own functions and on the whole presents the disaster situation. This biological structure-inspired architecture has three layers: 1) data substrate layer; 2) connectivity structure layer; 3) pathological manifestation layer, to simulate the three-layer structure of skin. First, a temporal feature extraction method is proposed, which can provide temporal correlations of each node. Second, a dimensional independent spacial feature extraction method is proposed, these first two methods form the connectivity structure layer, which is mainly composed of a spatio-temporal correlation model. Third, a structural health evaluation method for the pathological manifestation layer is proposed to fuse heterogeneous data and calculate multi-granularity structural risk with the features extracted from the connectivity structure layer. The experimental results show that MSHA can achieve not only accurate prediction and intuitive disaster situation, but also low energy consumption and longer lifetime. Note to Practitioners—This paper was motivated by the problem of effectively monitoring the structural health of infrastructure with wireless sensor networks. Existing methods for infrastructure structural health monitoring have space for improvement in maximizing the utilization of correlations between sensor nodes, temporal sequences, and between different sensor types, and fail to produce intuitive results to characterize structural health. In this paper, inspired by the multilayer structure of skin, we propose a new method for analyzing and presenting the structural changes of infrastructure under wireless sensor network data, which fully analyzes the relationship between sensor data in time, space, and type, and is able to predict the structural data in the future period and visually express the current safety situation with size and color information. In this paper, we mathematically describe the model construction method and feature transfer of the constructed intelligent monitoring method. We validate the proposed method using actual tunnel data. The experimental results show that the method is feasible and can accurately indicate the current safety situation in each area while achieving high accuracy in predicting future data, as well as good performance in terms of energy consumption and life cycle. In the future, we will explore optimal strategies for the placement of sensor nodes and improve the convenience and adaptability of the models across multiple scenarios.