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Volume 8, Number 2, June 2021

Sensor placement optimization is an attempt to reduce the cost and enhance the detection performance in structural health monitoring (SHM) systems. This paper aims at studying sensor placement optimization for SHM systems. The attention is paid to lamb wave or guided wave-based SHM (GWSHM). By using detection theory and Bayes risk framework the expected cost (loss) of decision making or Bayes risk for SHM system is minimized and the optimal detector is derived. The global detection and false alarm rate are used for quantifying the detector performance. In this framework the sensor coverage, directionality and probabilities of damage occurrence are all accounted for. The effect of cross-correlation among actuator-sensor pairs is then considered by presenting an appropriate model for covariance structure. Applying the genetic algorithm, the global false alarm rate is minimized for a target global detection rate and different levels of correlation. In addition, the receiver-operating characteristic (ROC) is determined to analyze the effect of correlation on the system performance and optimal arrangement. For demonstration of the effect of cross-correlation on damage detection a numerical analysis is carried out using ABAQUS standard. Finally, it is concluded that by increasing the correlation among actuator-sensor pairs, the performance of the SHM system decreases.

Key Words
structural health monitoring; optimal sensor placement; Bayes risk; detection theory; lamb wave

Department of Aerospace Engineering, Amirkabir University of Technology, 424, Hafez Ave, Tehran, Iran.

Urban rail transit is a critical infrastructure system that supports urban economic and social development. It has a significant mass transportation capacity while enables environmental benefits. Public transport is a way to resolve large-scale urban road traffic problems and contributes towards sustainable development. However, with the operations of railway vehicles on curves, unbalanced and undulated wears often appear on rails, especially on the low rail. This rail surface defect, so-called 'rail corrugation', directly affects the service life of rolling stocks and track components. The high-frequency vibrations caused by train-track interaction over rail corrugations also impair passenger ride comfort and generate excessive noises. In severe cases, the defects may even endanger the safe passage of a railway vehicle. In practice, rail corrugation has brought huge challenges to the reliable operations and maintenance of railway networks. With the continuous expansion of railway lines and the increasing traffic demands, any existing rail corrugation test method is not enough to meet the actual needs of track maintainers to promptly identify and mitigate rail surface defects. Therefore, this investigation aims to establish a new technique to prognose and classify rail corrugations efficiently and effectively. This study adopts D-track dynamic simulation package to obtain over thousands of vibration data in the form of axle box accelerations from train-track interactions under different conditions. Neural network models have been developed to recognize the rail corrugations and then classify their severity to aid the planning and prioritization of rail track maintenance activities. The models have been trained and tested using the vibration data, achieving the accuracy of over 90%. The optimal model has then been highlighted. The investigation has demonstrated the potential of the neural network to detect and classify rail corrugations, which can be used practically for curved track condition monitoring and maintenance planning.

Key Words
rail corrugation; dynamic analysis; artificial neural network; machine learning; monitoring; maintenance

School of Engineering, University of Birmingham, Birmingham B15 2TT, UK.

An improved unscented Kalman filter approach is implemented to estimate induced displacements and changes in structural properties (stiffness, frequencies and damping) during the forced dynamic response of multistory buildings to seismic excitations. The methodology is validated using a fiber-based nonlinear model of a 4- story 4-bays reinforced concrete (RC) frame building subjected to a set of earthquakes causing different levels of inelastic demand on the structure. The variation of the dynamic properties is successfully estimated by iterative updating the filter parameters. The estimated peak values of stiffness and damping reached during the seismic excitation agree with peak inelastic demand values and seem appropriate for detection and damage diagnosis of RC structures.

Key Words
health monitoring; kalman filter; reinforced concrete; earthquake; multiple degree of freedom; openSees

(1) Carlos A. Gaviria:
Civil Engineering Program, Facultad de Estudios a Distancia, Universidad Militar Nueva Granada, Colombia;
(2) Luis A. Montejo:
Department of Engineering Sciences and Materials, University of Puerto Rico at Mayaguez, Puerto Rico.

The fatigue of steel bridges poses a great threat to their safety and functionality. However, current approaches for fatigue management are largely based on heuristic design philosophies, physical testing, and bridge managers' experience. This paper proposes a closed lifecycle fatigue management driven by Digital Twin for steel bridges. To provide clarity around the concept, the definition of Digital Twin for steel bridges is given at first. Then eight functional modules supporting Digital Twin are outlined in detail, aiming to provide a reference for the future development of Digital Twin in fatigue management. Finally, the implementation mechanism of Digital Twin is further described over different phases during the bridge lifecycle. This paper also identifies two main obstacles for the development of Digital Twin: i) the lack of understanding of steel bridge fatigue, and ii) the insufficiency of the present technologies.

Key Words
Digital Twin; bridge maintenance; fatigue life evaluation; lifecycle management; steel bridges

(1) Fei Jiang, Youliang Ding:
Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, No. 2 Sipailou, Xuanwu District, Nanjing 210096, China;
(2) Yongsheng Song:
School of Architecture Engineering, Jinling Institute of Technology, No. 99 Hongjing Avenue, Jiangning District, Nanjing 211169, China;
(3) Fangfang Geng:
School of Architecture Engineering, Nanjing Institute of Technology, No. 1 Hongjing Avenue, Jiangning District, Nanjing 211167, China;
(4) Zhiwen Wang:
Shenzhen Express Engineering Consulting Co. Ltd., 268 Meiao 1st Road, Futian District, Shenzhen 518000, China.

The temperature induced response of long-span cable-stayed bridge in cantilever state is significant, which is of great interest to study the temperature characteristics during construction period. A method of analyzing the eigenvalue and its extremum of daily temperature based on cubic spline function (CSF) is proposed. By setting the fixed time interval reasonably, introducing variable time interval and extracting nodes at the MinMax of daily temperature, the obtained CSF can approach the measured temperature curve with high accuracy. Based on CSF, the temperature characteristics at three levels of measuring point, section and component are analyzed in turn. The temperature monitoring data of a cable-stayed bridge with main span of 938 m and side span of steel-concrete composited box girder (CBG) during construction are analyzed. The results show that the temperature variation of steel box girder is remarkable; the steel beam of CBG is similar to steel box girder before composited, and it turns stable after composited; the influence of PE color on cable temperature is notable than that of the cable specification; as blue PE cable, the temperature difference of cable vs pylon and cable vs CBG exceed 17°C and 13°C.

Key Words
cable-stayed bridge; temperature monitoring; construction period; cubic spline function; composite box girder; steel box girder

(1) Xiudao Mei, Yiyan Lu:
School of Civil Engineering, Wuhan University, 299 Bayi Road, Wuhan City, Hubei Province, China;
(2) Jing Shi:
Department of Bridge Health Monitoring, State Key Laborary for Health and Safety of Bridge Structures, 103 Jianshe Avenue, Wuhan City, Hubei Province, China.

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