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In the past decade, the emerging machine vision-based measurement technology has gained great concerns among civil engineers due to its overwhelming merits of non-contact, long-distance, and high-resolution. A critical issue regarding to the measurement performance and accuracy of the vision-based system is how to identify and eliminate the systematic and unsystematic error sources. In this paper, a vision-based structural displacement measurement system integrated with a digital image processing approach is developed. The performance of the developed vision-based system is evaluated by comparing the results simultaneously obtained by the vision-based system and those measured by the magnetostrictive displacement sensor (MDS). A series of experiments are conducted on a shaking table to examine the influence factors which will affect the accuracy and stability of the vision-based system. It is demonstrated that illumination and vapor have a critical effect on the measurement results of the vision-based system.

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In comparison with above-ground structures, the investigation of underground space structures still faces great challenges because of the extremely complicated constitutive relationships of the soils or rocks. Implementation of structural health monitoring (SHM) systems on the underground structures such as tunnels commencing from the construction stage may be of help in understanding their operational behaviors and long-term trends. This paper explores the application of the fiber Bragg grating (FBG) sensing technology for safety monitoring during railway tunnel construction. An FBG-based temperature monitoring system is first developed for real-time temperature measurement of the frozen soils during freezing construction of a metro-tunnel cross-passage. Through in-situ deployment of FBG-based liquid-level sensors, the subgrade settlement of a segment of a high-speed rail line is then monitored in an automatic manner during construction of an undercrossing tunnel. The field results indicate that the FBG sensors are robust and reliable in perceiving temperature and strain variations even in harsh environments.

A statistical analysis of the wind speed and wind direction serves as a solid foundation for the wind-induced vibration analysis. The probabilistic modeling of wind speed and direction can effectively characterize the stochastic properties of wind field. The joint distribution model of wind speed and direction involves a circular distribution and has a multimodal characteristic. In this paper, the finite mixture distribution model is introduced and used to represent the joint distribution model that is comprised of the mixture Weibull distributions and von Mises distributions. An extended parameters estimation algorithm for multivariate and multimodal circular distributions is proposed to construct the joint distribution model. The proposed algorithm estimates the component parameters, mixture weight of each component and the number of components successively by an iterative process. The major improvement is accomplished by adding a circular distribution model. The effectiveness of the proposed algorithm is verified with numerical simulations and one-year field monitoring data and compared with the expectation maximization algorithm-based angular-linear approach in terms of the Akaike's information criterion and computing time. The results indicate that the finite mixture model represents the joint distribution model of wind speed and direction well and that the proposed algorithm has a good and time-saving performance in parameter estimation for multivariate and multimodal models.

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