In this paper, we propose a new decentralized method for structural health monitoring based on substructure dynamical
parameter identification, which can be executed in parallel on multiple smart sensor nodes. A decentralized Kalman filter
algorithm is applied for modal parameter identification of structures using multi-input and multi-output ARX model.
Furthermore, network communication topology is investigated using the substructure approach for finding a practical
application of the decentralized algorithm. Numerical simulation studies are carefully performed for three types of
network topologies and two types of model structures. Identification tests using the seismic observation data of existing
5-story building are also conducted. The results show the feasibility of the proposed decentralized method.
The authors have been studying the strain sensitive materials which are based on conductivity change resulting from structural change in percolation system. In this study, we have developed a maximum strain memory sensor, which enables to detect damage to structures easily even after a large earthquake. To confirm the performance as the sensor, tensile tests embedded into concrete specimen have been conducted. As a result, it is discovered that this sensor is sufficiently effective to diagnose cracks in the concrete structure.
In recent years, the importance of Structural Health Monitoring has been recognized but an SHM system still confronts serious problems related to complexity and cost in practical use. To solve these problems, the authors have developed the simple and smart SHM system by integrating self-diagnosis material and a wireless data measurement device. By installing this SHM system, it is possible to detect damage to structures easily even after a large earthquake or other disaster and also to inspect possible deterioration of a structure in a short time. As a practical matter this SHM system is expected to be very reliable, and when it is mass-produced it should have a low cost. To confirm the utility of the damage detection of a building after a large earthquake, the pre-production system was installed in a specimen simulating the beam-to-column connection part in a mid-size conventional reinforced concrete building, and a loading test was performed on the specimen. The effectiveness of the proposed system is demonstrated by the test results.
Proc. SPIE. 5057, Smart Structures and Materials 2003: Smart Systems and Nondestructive Evaluation for Civil Infrastructures
KEYWORDS: Detection and tracking algorithms, Data modeling, Sensors, Error analysis, Earthquakes, Structural health monitoring, Modal analysis, Damage detection, System identification, Systems engineering
This paper proposes damage detection algorithm of a structural health monitoring (SHM) system for a seismic isolated building. The proposed algorithm consists of the multiple-input multiple-output (MIMO) modal analysis and the physical parameter identification. A story stiffness as a direct damage index of the structure is identified using complex modal properties obtained by subspace-based state space model identification (4SID). This algorithm is tuned for seismic isolated structures using substructure approach (SSA). Of a seismic isolated structure, the isolation layer and superstructure are treated as separate substructures as they are distinctly different in their dynamic properties. The damage scenario for a seismic isolated structure is much simpler and more accurate than for a conventional building. Our strategy is to maximize the benefit of this simplicity. The effectiveness is verified through the numerical analysis and experiment. The method is finally applied to an existing building in Japan. The monitoring target is a 7-story seismic isolated building with the gross floor area of 18606m2 and with total height of 31m. This study shows potential to build a simple and reliable SHM system for seismic isolated buildings.