Strain is one of the key concerns of structural health monitoring (SHM), and effective measurement of structural strain is very important for structural health condition assessment. The purpose of this paper is to investigate the application of the micro image strain sensing (MISS) sensor based on smartphone and microscope for measuring strain parameters of structural members. Firstly, metal tensile tests were performed to verify the effectiveness of the MISS sensor, and the strain results measured by the fiber Bragg grating (FBG) sensor and the MISS sensor were compared. The results showed that the mean error between the MISS and FBG sensor measured results is 7.1 με in the metal tensile test. Then, a concrete beam bending test was carried out; the mean relative error of strain values between the FBG sensor and the MISS sensor is 2.5%. Overall, the strain measurements of the MISS sensor matched well with those from the FBG sensor in each experiment. This study validates the reliability and accuracy of the MISS sensor for strain detection and the MISS sensors can play a more powerful role in SHM due to their robustness, simplicity, and low cost.

Structural health monitoring (SHM) is of great significance for post-earthquake damage assessment. Smartphone-based monitoring techniques provide the possibility to perform crowdsensing for all buildings in urban regions after an earthquake. However, this idea still faces many difficulties and is hard to realize. Fortunately, the development of game engines provides the opportunity for simulating this kind of experiment. The main objective of this study was to use Unity to simulate the whole process when a city is struck by an earthquake that consists of one main shock and one aftershock. During the emergency response, the citizens and the “city brain” in Unity, named Ground Eye, cooperate to finish the task of taking refuge and collecting data for regional damage assessment. Some basic assumptions were made first. Then the city model was established in Unity, and the behaviors of the citizens were directed by the behavior tree artificial intelligence (AI). OpenSees was utilized to determine the monitoring demand and simulate the monitoring results. A GUI was built to exhibit the data during the whole process. The results show that the evacuation and monitoring plan is feasible. The simulation framework presented in this paper can be used in other SHM application scenarios.

The life-cycle inspection of civil infrastructure can guide decisions on structural safety and reliability. This paper proposes a strategy for smartphone-based public participation in the life-cycle inspection of civil infrastructure (SPIC). The SPIC strategy consists of three parts: participants, Urban Brain, and Global View. Next, the feasibility of this strategy is verified by simulation experiments implemented in Unity3D. Civil infrastructure inspection modes include routine inspection, focus inspection, and emergency inspection. The activation and transition of the three inspection modes are tested. Test results show that routine inspection with public participation could timely identify pre-set damaged civil infrastructures. All abnormal civil infrastructures are observed by the Urban Brain. The focus inspection mode is activated when an abnormal civil infrastructure is detected, and the focus inspection provides a detailed and professional assessment of the abnormal civil infrastructure. Then, the inspection of all civil infrastructures in emergency inspection mode is completed in the test. Furthermore, this paper carries out a factor sensitivity analysis of this strategy. The proposed strategy establishes the mechanism for the assign-accept-feedback inspection task. In addition, the simulation performed in Unity3D is one of the next alternatives for large-scale urban disaster prevention and mitigation experiments in the real world. The SPIC strategy can effectively enhance the disaster prevention and mitigation capabilities of civil infrastructure.

Micro-image strain sensing (MISS) is an innovative technology to measure strain within a measurement range of ±8300 microstrains. It has been proved to be effective and satisfy all requirements in the field of structural health monitoring. However, there is still room for improvement and extend the range of measurement. In this paper, an improved method is proposed to increase the measurement range of strain and displacement. Moreover, various tests were conducted to verify the efficiency of the improved method. The results showed that the modified method is efficient and accurate and can be readily used to extend the measurement range of both strain and displacement. This research will likely help stimulate the development of multifunctional sensors to obtain abundant useful information about structures in the field of structural health monitoring. It will allow measuring strain and displacement, which require different levels of accuracy, using one unified sensor.

Strain measurements have a significant role in evaluating the condition of various structural types and have become an essential component in the area of structural health monitoring. However, there are some limitations in the current means of strain measurement, and this study aims to improve these methods. We have designed a novel strain measurement method based on template matching algorithms and microscopic vision techniques, developed a new sliding strain sensor, and paired it with a new microscope to realize strain measurement. The method has the function of remote wireless acquisition with a cell phone, which is more widely applicable. In the laboratory performance testing, the zero drift of the sensor is mainly concentrated in the fluctuation range of ±2 με, and the effective range reaches nearly 40,000 με. In the comparison experiments with the linear variable differential transformer, the maximum error of the static loading is only 5 με, and the maximum error rate of the dynamic loading is less than 1%, which proves that it has a relatively high accuracy. Finally, the short-term real-time monitoring of the local structure of the footbridge was accomplished, and the strain changes on the surface of the structure were captured instantly, stably, and efficiently in the actual measurements. The proposed strain measurement system has the advantages of high accuracy, a low cost, convenient measurement, and wide applicability, and it provides a novel alternative means for strain measurement in the field of structural health monitoring.

The authors present a new scheme that extends the correlation based fast training of echo cancellers introduced by Long and Ling (1990) to allow for ultra-high-speed initialization of a full-duplex data transmission system over two-wire lines. A fast training procedure is derived to coestimate the near echo and the far echo with bulk delay, and the channel responses. The effects of the noise and symbol rates' offset between two ends are examined in terms of both mean square error and SNR. Both the theoretical analysis and computer simulations show that there are three degrading factors due to symbol rates' offset. The new algorithm delivers fst convergence speed with the same computational requirements as those of stochastic-gradient algorithms and reduces the tap-setting time to half of that required by the traditional half-duplex fast training scheme.