Current Issue : January - March Volume : 2021 Issue Number : 1 Articles : 5 Articles
The safety of prestressed steel structures in service has been studied widely. However, traditional safety assessment methods for\nprestressed steel structures involve few sample points, do not provide accurate predictions, and consume substantial human and\nmaterial resources. The digital twin technology can be used to monitor the structural behavior, state, and activity of a steel\nstructure throughout its life cycle, which is equivalent to performing a safety assessment of the structure. The purpose of this study\nis to establish a digital twin multidimensional model of prestressed steel structures. Based on this model, the support vector\nmachine and prediction model are trained using the relevant structural history data, and the safety risk level of the structure is then\npredicted based on the measured data. Finally, a proportional reduction model of the wheel-spoke cable truss structure is used to\nverify the feasibility of the proposed method. The results show that digital twin technology can achieve real-time monitoring of\nprestressed steel structures in use and can provide timely predictions of the safety level. This represents a new method for the safety\nrisk assessment of prestressed steel structures....
Rail irregularity is the leading cause of enhancing train-track coupling vibration and, therefore, should be studied in detail for\nsafety requirements. In this study, the differences between existing rail irregularities without being subjected to an earthquake\nbetween different countries were first studied. Results show that existing power spectrum density and time-domain displacement\nsamples of rail irregularities in the American code are the largest, while the irregularities of the Germany railway are higher than\nthose of China in a specific range of rail wavelengths. Afterward, the effects of earthquake intensity, soil site, and duration on the\nrail irregularity of a Chinese typical high-speed railway bridge were investigated. For this purpose, a finite element model was\nestablished and validated by the shaking table test of a 1/12-scaled high-speed railway bridge experimental specimen. The\ncalculation results indicated that the influences of earthquakes on the rail alignment irregularity were evident....
To improve the high-temperature rutting resistance of asphalt pavements, an inverted asphalt pavement structure (IAPS), 4 cm\nAC-13 mixture + 8 cm AC-25 mixture + 6 cm AC-20 mixture + 54 cm cement-stabilized macadam, was proposed herein by\nconsidering engineering practice, theoretical calculation, and analysis. A rutting prediction equation of asphalt pavements was\nthen proposed via rut-development trends found by laboratory 18 cm thick rutting test. Subsequently, the rutting resistance of the\nIAPS was evaluated..................
This paper aims to further enhance the accuracy and efficiency of large bridge structural health monitoring (SHM) through\nnoncontact remote sensing (NRS). For these purposes, the authors put forward an intelligent NRS method that collects the\nholographic geometric deformation of the test bridge, using the static image sequences. Specifically, a uniaxial automatic cruise\nacquisition device was designed to collect the dynamic and static images on bridge facade under different damage conditions.\nConsidering the strong spatiotemporal correlations of the sequence data, the relationships between the time history images in six\nfixed fields of view were identified through deep learning under spatiotemporal sequences. On this basis, the behavioral features of\nthe bridge structure were obtained under vehicle load. Finally, the global holographic deformation of the test bridge and the\nenvelope spectrum of the global holographic deformation were derived from the deformation data. The research results show that\nthe output data of our NRS method were basically consistent with the finite-element prediction (maximum error: 11.11%) and\ndial gauge measurement (maximum error: 12.12%); the NRS method is highly sensitive to the actual deformation of the\nbridge structure under different damage conditions and can capture the deformation in a continuous and accurate manner.\nCompared with the limited number of measuring points, holographic deformation data also shows higher sensitivity in\ndamage identification....
In order to improve the strength of civil engineering structure, a semiactive control model of civil engineering structure based on\nneural network is proposed, and the control constraint parameter model of semiactive regulation of civil engineering structure is\nconstructed. Combined with the controlled object model, the semiactive control model of civil engineering structure is designed,\nthe mechanical analysis model of civil engineering structure is established, and the semiactive regulation of civil engineering\nstructure is carried out by the small disturbance suppression method. The semiactive adjustment of civil engineering structure is\ncarried out by using the structural strength fusion tracking method. Taking the internal strength and shock yield response of\ncivil engineering structure as constraint parameters, the semiactive control of civil engineering structure is carried out and PID\nneural network is used to optimize the control system. The simulation results show that the semiactive control of civil\nengineering structure with this method has good stability, and the strength and yield response strength of civil engineering\nstructure are improved, and it has good control efficiency....
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