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Traffic load is one of the important external loads of bridges and simulating the site-specific traffic load is important for bridge condition assessment. This study proposes a new microscopic model to simulate the stochastic traffic load of any time period at bridge site based on weigh in motion (WIM) data. A model for simulating the microscopic interaction between vehicles was first proposed, by which free driving, car following and sudden braking can be reasonably simulated. Axle-weight information from the WIM data was embedded into the model to precisely simulate the vehicle load. A new vehicle sequence generation method based on WIM data was then proposed to generate the site-specific vehicle sequence of any time period with consideration of non-stationarity. Finally, a framework for simulating the site-specific stochastic traffic load is established. The proposed framework is validated through application based on realistic WIM data in traffic-flow simulation and load effect quantification. Comparisons of simulation results with the existing methods were also conducted to show the reasonability and novelty. The results indicate that the proposed framework can produce reasonable site-specific stochastic traffic load over any time period based on WIM data. [ABSTRACT FROM AUTHOR]

Identifying precursor information to rock failure is key to rock dynamic disaster warning. To solve the problem that a single in-situ monitoring method cannot accurately identify the precursors to rock failure, this paper combined a digital image correlation method, thermal infrared radiation and acoustic emission technology to conduct a multi-physical field joint monitoring experiment of granite under three-point bending. The information about strain, thermal infrared and acoustic emission of the rock brittle failure process could be obtained, and the variations in multi-physical field indices of the rock brittle failure process were analysed. Based on the theory of critical slowing-down, the study of the multi-physical field effects of critical slowing-down characteristics in the rock failure process, and further exploration of the precursor characteristics of the brittle failure of rock were undertaken. The results indicate that the strain, infrared, and acoustic emission parameters show sudden changes during brittle failure of granite. The lag step size has little effect on the variance of changes in multi-physical field parameters and exerts a similar influence on the changes in the autocorrelation coefficient curve. The degree of influence of the window length on the variance and autocorrelation coefficient curves of multi-physical field parameters is as follows: infrared field > strain field > acoustic emission field. Compared with the autocorrelation coefficient, the variance parameter is more universal and is better when the critical slowing-down theory is used to investigate the characteristics of multi-physical fields. The four parameters of the strain field variance, infrared field variance, the variance and autocorrelation coefficient for acoustic emission field increase sharply before fracture, representing precursor information to the brittle failure of granite. The four parameters can be used as disaster warning indicators in rock. Multi-mode joint monitoring and a comprehensive analysis of multi-physical information are beneficial to accurately determine the precursor points of rock failure disaster. [ABSTRACT FROM AUTHOR]

Identifying, tracking and warning of the vortexed induced vibration (VIV) are essential for long-span suspension bridges because VIV might cause the driving safety problem. This study proposes an automatic method to identify and track the start and evolution of the VIV. Two feature indices, i.e. the energy concentration coefficient (ECC) and similarity ratio of amplitude (SRA), are utilised to form a feature index vector to identify and track the VIV. The Hilbert transform is utilised to process the vibration signal, based on which the SRA can be extracted. The k-means clustering method is introduced to automatically identify and track the VIV through automatically separating the feature index vectors into several clusters, and multi-level warming is established based on the acceleration root mean square (ARMS) value. The proposed method is validated using the in-field monitoring data of a suspension bridge suffering the VIV. The results indicate that the proposed automatic method can successfully distinguish the VIV from ambient vibration and can identify the forming of the VIV and track the evolution without any manual intervention, even for the VIV with low vibration amplitude. The multi-level warning can also be achieved based on the ARMS value when the VIV is identified. [ABSTRACT FROM AUTHOR]

Spatial prediction is essential for obtaining the spatial distribution of geographic variables and selecting appropriate covariates for this process can be challenging, especially for non-expert users. For easing the burden of selecting the appropriate covariates, two case-based reasoning strategies, namely the most-similar-case and covariate-classification strategies, have been proposed for automated covariate selection. The former may suggest nonessential covariates due to its case-level reasoning way. And the latter with covariate-level reasoning may overlook related covariates and recommend fewer covariates than the case-level reasoning. In this study, we propose a new strategy of integrating case-level and covariate-level reasoning to effectively leverage the strengths of both previous strategies while also addressing their limitations. The proposed strategy is validated through a case study of automatically selecting covariates for digital soil mapping under reasoning with a case base containing 189 cases. The leave-one-out evaluation demonstrated that our proposed strategy outperformed the previous two strategies. [ABSTRACT FROM AUTHOR]