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This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure.
RESUMO
The precision of target-based registration is related to the geometry distribution of targets, while the current method of setting the targets mainly depends on experience, and the impact is only evaluated qualitatively by the findings from empirical experiments and through simulations. In this paper, we propose a new quantitative evaluation model, which is comprised of the rotation dilution of precision (, assessing the impact of targets' geometry distribution on the rotation parameters) and the translation dilution of precision (, assessing the impact of targets' geometry distribution on the translation parameters). Here, the definitions and derivation of relevant formulas of the and are given, the experience conclusions are theoretically proven by the model of and , and an accurate method for determining the optimal placement location of targets and the scanner is proposed by calculating the minimum value of and . Furthermore, we can refer to the model ( and ) as a unified model of the geometric distribution evaluation model, which includes the model in GPS.
RESUMO
OBJECTIVES: Clinical evaluation of dysphonic voices involves a multidimensional approach, including a variety of instrumental and noninstrumental measures. Acoustic analyses provide an objective, noninvasive and intelligent measures of voice quality. Based on sound recordings, this paper proposes a new classification method of voice disorders with HHT and KNN. METHODS: In this research, 12 features of each sample is calculated by HHT. Based on the algorithm of Linear Prediction Coefficient (LPCC), a sample can be characterized by 9 features. After each sample is expressed by 21 features, the classifier is constructed based on KNN. In addition, classifier based on KNN was further compared with random forest and extra trees classifiers in relation to their classification performance of voice disorder. RESULTS: The experiment results revel that classifier based on KNN showed better performance than other two classifiers with accuracy rate of 93.3%, precision of 93%, recall rate of 95%, F1-score of 94% and the area of receiver operating characteristic curve is 0.976. CONCLUSIONS: The method put forward in this paper can be effectively used to classify voice disorders.