This case study provides feasibility analysis of adapting Spiking Neural Networks (SNN) based Structural Health Monitoring (SHM) system to explore low-cost solution for inspection of structural health of damaged buildings which survived after natural disaster that is, earthquakes or similar activities. Various techniques are used to detect the structural health status of a building for performance benchmarking, including different feature extraction methods and classification techniques (e.g., SNN, K-means and artificial neural network etc.). The SNN is utilized to process the sensory data generated from full-scale seven-story reinforced concrete building to verify the classification performances. Results show that the proposed SNN hardware has high classification accuracy, reliability, longevity and low hardware area overhead.

With the evolvement, standards have changed, mobile Internet technology has also been upgraded, and it has also driven the development of smart objects mobile. With the continuous development of smart objects mobile, the bottleneck of small node size and low battery energy storage has not been solved in the end, which makes the research of wireless sensor network energy-saving technology become the focus, and the improvement of routing technology is an effective way to improve energy-saving technology. From the data transmission energy consumption of smart objects mobile, the routing algorithm of smart objects mobile is discussed and analyzed and the classical representative LEACH is the object of in-depth research. Routing algorithms can easily and reliably process network data and make the network work well and are widely used in highly secure military systems and smaller commercial networks. Aiming at these deficiencies, a corresponding improved algorithm is proposed, and it is tested through simulation and specific experiments to verify the correctness and the systems reliability. The SMPSO-BP algorithm converges when the number of iterations is about 600, which is earlier than the LEACH algorithm and the improved LEACH algorithm, so the SMPSO-BP algorithm is due to the other two algorithms. In the wireless sensor network routing energy consumption experiment, in addition, the SMPSO-BP algorithm uses less energy than the other two methods. Therefore, the energy-saving algorithm under the neural network data fusion mechanism is still feasible.