Structural health monitoring (SHM) has become an important research problem which has the potential to monitor and ensure the performance and safety of civil structures. Traditional wire-based SHM systems require significant time and cost for cable installation. With the recent advances in wireless communication technology, wireless SHM systems have emerged as a promising alternative solution for rapid, accurate and low-cost structural monitoring. This paper presents a newly designed integrated wireless monitoring system that supports real-time data acquisition from multiple wireless sensing units. The selected wireless transceiver consumes relatively low power and supports long-distance peer-to-peer communication. In addition to hardware, embedded multithreaded software is also designed as an integral component of the proposed wireless monitoring system. A direct result of the multithreaded software paradigm is a wireless sensing unit capable of simultaneous data collection, data interrogation and wireless transmission. A reliable data communication protocol is designed and implemented, enabling robust real-time and near-synchronized data acquisition from multiple wireless sensing units. An integrated prototype system has been fabricated, assembled, and validated in both laboratory tests and in a large-scale field test conducted upon the Geumdang Bridge in Icheon, South Korea.

A damage detection technique, which does not rely on any past baseline signals, is proposed to assess damage in composite plates by using an enhanced time reversal method. A time reversal concept of modern acoustics has been adapted to guided-wave propagation to improve the detectability of local defects in composite structures. In particular, wavelet-based signal processing techniques have been developed to enhance the time reversibility of Lamb waves in thin composite laminates. In the enhanced time reversal method, an input signal at an excitation point can be reconstructed if a response signal measured at another point is reemitted to the original excitation point after being reversed in a time domain. This time reversibility is based on linear reciprocity of elastic waves, and it is violated when nonlinearity is caused by a defect along a direct wave path. Examining the deviation of the reconstructed signal from the known initial input signal allows instantaneous identification of damage without requiring the baseline signal for comparison. The validity of the proposed method has been exemplified through experimental studies on a quasi-isotropic laminate with delamination.

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian process (GP) regression, a nonparametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed by any part of the machine using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

Calibration, on the basis of data from centrifuge and shake table experiments, continues to promote the development of more accurate computational models. Capabilities such as coupled solid–fluid formulations and nonlinear incremental-plasticity approaches allow for more realistic representations of the involved static and dynamic/seismic responses. In addition, contemporary high-performance parallel computing environments are permitting new insights, gained from analyses of entire ground-foundation-structural systems. On this basis, the horizon is expanding for large-scale numerical simulations to further contribute toward the evolution of more accurate analysis and design strategies. The studies presented in this paper address this issue through recently conducted three-dimensional (3D) representative research efforts that simulate the seismic response of (1) a shallow-foundation liquefaction countermeasure, (2) a pile-supported wharf, and (3) a full bridge-ground system. A discussion of enabling tools for routine usage of such 3D simulation environments is also presented, as an important element in support of wider adoption and practical applications. In this regard, graphical user interfaces and visualization approaches can play a critical role.