Fatigue and corrosion are the main reasons for the failure of stay cables. Traffic load also plays a significant role in cable fatigue. To study the fatigue life of stay cables under the combined action of traffic load and corrosion, this study originally deduced the fatigue probability model for steel wires, considering the stress range and average stress based on the nonlinear fatigue damage model. Then, the weight loss rate was introduced into the fatigue probability model for steel wires in order to obtain the fatigue life probability model for corroded steel wires. Subsequently, a Monte Carlo simulation (MCS) was conducted to predict the fatigue life of stay cables under different guaranteed probabilities. Finally, using a cable-stayed bridge on the Yangtze River in China as an example, the fatigue life of the stay cables in the bridge under the combined effects of different corrosive environments and measured traffic loads was evaluated. Results indicate that both the stress range and mean stress affect the fatigue life of the steel wire. The fatigue life of stay cables is determined by a few steel wires with shorter lives, and the stress redistribution accelerates the failure of the inner wire of the stay cables. The fatigue life of the stay cables on background engineering is much less than the design life of the bridge under the combined actions of a middle-level or high-level corrosive environment and traffic load.

Human falls are the premier cause of fatal and nonfatal injuries among older adults. The health outcome of a fall event is largely dependent on rapid response and rescue of the fallen elder. Being able to provide an accurate and fast fall detection will dramatically improve the health outcomes of the older population and reduce the associated healthcare cost after a fall. To achieve the goal, a multi-features semi-supervised support vector machines (MFSS-SVM) algorithm utilizing measurements from structural floor vibration obtained through accelerometers is proposed in this study to detect falling events with limited labeled samples. In this MFSS-SVM algorithm, the peak value, energy, and correlation coefficient of the accelerometer signal are used as classification features. The performance of the proposed algorithm was validated with laboratory experiments among activities including falling, walking, free jumping, rhythmic jumping, bag dropping, and ball dropping. To further illustrate the performance of the algorithm, a benchmark database was adopted and expanded to test its ability to accurately identify falling, compared with the algorithm used in the benchmark study. Results show that by using the proposed algorithm, the falling events can be identified with high accuracy and confidence, even with small training datasets and test nodes.

Structural strain under external environmental loads is one of the main monitoring parameters in structural health monitoring or dynamic tests. This paper presents a wireless strain sensor network (WSSN) design for monitoring structural dynamic strain field. A precision strain sensor board is developed and integrated with the IRIS mote hardware/software platform for multichannel strain gauge signal conditioning and wireless monitoring. Measurement results confirm the sensor’s functionality regarding its static and dynamic characterization. Furthermore, in order to verify the functionality of the designed wireless strain sensor for dynamic strain monitoring, a cluster-star network evaluation system is developed for strain modal testing on an experimental steel truss structure. Test results show very good agreement with the finite element (FE) simulations. This paper demonstrates the feasibility of the proposed WSSN for large structural dynamic strain monitoring.

Smart wireless sensors have been recognized as a promising technology to overcome many inherent difficulties and limitations associated with traditional wired structural health monitoring (SHM) systems. Despite the advances in smart sensor technologies, on-board computing capability of smart sensors has been considered as one of the most difficult challenges in the application of the smart sensors in SHM. Taking the advantage of recent developments in microprocessor which provides powerful on-board computing functionality for smart sensors, this paper presents a new decentralized data processing approach for modal identification using the Hilbert-Huang transform (HHT) algorithm, which is based on signal decomposition technique. It is shown that this method is suitable for implementation in the intrinsically distributed computing environment found in wireless smart sensor networks (WSSNs). The HHT-based decentralized data processing is, then, programmed and implemented on the Crossbow IRIS mote sensor platform. The effectiveness of the proposed techniques is demonstrated through a set of numerical studies and experimental validations on an in-house cable-stayed bridge model in terms of the accuracy of identified dynamic properties.

Optimal multi-type sensor placement has gained considerable attention in the structural health monitoring field during the past few years. Although a structural response reconstruction-oriented optimization method for a multi-type sensor has been developed, the challenge of information redundancy in the collected signal deserves further investigation. To tackle this challenge, this paper presents a two-stage optimization framework for response reconstruction with the capability to reduce the multi-type sensor information redundancy. In the first stage, the optimization of multi-type sensor placement for response reconstruction is performed to initially determine the optimal sensor deployment scheme. After the optimal sensor locations are selected, the second-stage optimization introduces a metric, called the distance coefficient, to evaluate the information independence level between sensor locations with the goal of reducing the sensor information redundancy. A numerical study on a bridge model is first performed to evaluate the feasibility of the proposed framework, after which a lab-scale physical bridge model is tested to validate its effectiveness. Both the numerical and experimental results demonstrate that the proposed two-stage optimization framework can reduce the sensor information redundancy and, at the same time, produce a satisfying result in the response reconstruction of key locations.