Safety Prediction Using Vehicle Safety Evaluation Model Passing on Long‐Span Bridge with Fully Connected Neural Network

Yang, Yang; Yang, Lin; Wu, Bo; Yao, Gang; Li, Hang; Smythe, Robert A.

doi:10.1155/2019/8130240

Cited by 12 publications

(12 citation statements)

References 51 publications

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“…For the fourth and aforementioned modes, the modes of the two models are quite different. e main reason is that the two models have different boundary conditions [22][23][24][25].…”

Section: Dynamic Characteristicsmentioning

confidence: 99%

Dynamic Characteristics and Time‐History Analysis of Hydraulic Climbing Formwork for Seismic Motions

Yao

Guo

Yang

et al. 2021

Advances in Civil Engineering

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With the widespread use and increasing cycle life of climbing formwork to construct high-rise buildings in earthquake-prone areas, the risk of earthquakes during the construction period increases. Hence, it is necessary to analyze the seismic response of climbing formwork. According to actual climbing formwork in the super high-rise office building of Wanda Plaza in Kunming, China, the finite element model of the climbing formwork is established on the Ansys platform. The correctness of the model is verified by comparing the natural frequencies of the actual climbing formwork and the finite element model. The time-history analysis of the climbing formwork subjected to earthquakes of varying strong magnitudes is carried out. The maximum displacement position and maximum von Mises stress position of the climbing formwork under different working conditions are determined, and the seismic response of the climbing formwork is analyzed. It has been found that when the formwork is under construction, the maximum displacement position of the climbing formwork is at the center of the long beam of the upper platform, and the maximum von Mises stress position is the joint of the outer pole of the main platform and tripod. Under the climbing condition, the maximum displacement position of the climbing formwork is at the top of the outer pole of the upper platform, and the maximum von Mises stress position is the joint of the beam of the tripod and guide rail. The climbing formwork is partially damaged under the simulated earthquake. However, the displacement is large, and some components have reached the yield state. It is recommended to strengthen the connection between the upper platform and the guide rail and enhance the strength and rigidity of the outer pole and tripod. Climbing formwork is more sensitive to horizontal earthquakes and has minimal sensitivity to vertical earthquakes. The structure attached to the climbing formwork will reduce its sensitivity to earthquakes. The research results are of practical significance for seismic design and improvement of climbing formwork.

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“…For the fourth and aforementioned modes, the modes of the two models are quite different. e main reason is that the two models have different boundary conditions [22][23][24][25].…”

Section: Dynamic Characteristicsmentioning

confidence: 99%

Dynamic Characteristics and Time‐History Analysis of Hydraulic Climbing Formwork for Seismic Motions

Yao

Guo

Yang

et al. 2021

Advances in Civil Engineering

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“…It composes of six prestressed I-girders at 2.8 m spacing with a 200 mm concrete deck slab and 50 mm asphaltic concrete surface layer as shown in Figure 4. The cross-sectional area and moment of inertia of each I-girder are 0.64 m 2 and 0.2422 m 4 , respectively. The elastic modulus of concrete of the bridge is 29 GPa and the material density is 2400 kg/m 3 .…”

Section: Numerical Studymentioning

confidence: 99%

“…However, this approach suffers from the drawback of a large capital cost as well as the closure of the bridge. In addition, the massive data monitored by the sensors is difficult to process effectively [4]. All these characteristics have limited the application and promotion of direct measurement methods in bridge health monitoring.…”

Section: Introductionmentioning

confidence: 99%

Feasibility Study of Tractor-Test Vehicle Technique for Practical Structural Condition Assessment of Beam-Like Bridge Deck

et al. 2020

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The tractor-test vehicle technique of non-destructive testing for indirect measurement of the modal properties of a bridge deck is revisited in this paper with several improvements for possible practical application to the structural condition assessment of a beam-like bridge deck. The effect of damping of the vehicle-bridge system is considered and the modal properties from only the first vibration mode of the structure will be used for a quick and simple assessment. The two test vehicles are designed to have the same modal frequency and damping ratio but with parameters in the follower No.2 test vehicle proportional to those in the follower No.1 test vehicle. This effectively removes the effect of road surface roughness in the response of an equivalent vehicle such that the error in the subsequent condition assessment is reduced. Through data collected on-sitetransmitted to theremote computer platform, a simple technique based on the moment-curvature relationship acceptable to practical engineers is adopted for the condition assessment with improvements in the estimation of the element bending stiffness of the deck. Scenarios with different damping, vehicle speed, road surface roughness, and local damages in the bridge structure are studied with or without temperature effect in the measurement. Through numerical simulations and field tests, the tractor-test vehicle technique of non-destructive testing with the proposed modifications and improvements has been demonstrated to give consistently accurate estimates of the element bending stiffness of the bridge deck but with a small error close to the end of the deck.

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“…This paper applies these two methods to the template system for the first time. In previous studies, we have successfully applied neural networks to the safety assessment of long-span bridges and achieved good results [26]. The timeliness of the structure monitoring sequence is more obvious than the safety assessment of long-span bridges, so we model this using the ARMA model and the BPNN model [27].…”

Section: Introductionmentioning

confidence: 99%

Post-Processing of High Formwork Monitoring Data Based on the Back Propagation Neural Networks Model and the Autoregressive—Moving-Average Model

Yang

Yao

2021

Symmetry

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Many high formwork systems are currently equipped with health monitoring systems, and the analysis of the data obtained can determine whether high formwork is a hazard. Therefore, the post-processing of monitoring data has become an issue of widespread concern. In this paper, we discussed the fitting effect of the symmetrical high formwork monitoring data using the autoregressive–moving-average (ARMA) model and the back propagation neural networks (BPNN) combined model to process. In the actual project, the symmetry of the high formwork system allows the analysis of local monitoring results to be well extended to the whole. For the establishment of the ARMA model, the accurate judgment of the model order has a significant impact. In this paper, back propagation neural networks (BPNN) are used to simulate the ARMA process. The order of the ARMA model is estimated by determining the optimal neural network structure, which is suitable for linear or nonlinear sequences. We validated this approach from the ARMA model data simulated in Monte Carlo and compared it with the Akaike information criterion (AIC) and Bayesian information criterion (BIC). The length of the sequence, the coefficients and the order of the ARMA model are considered as factors that influence the judgment effect. Under different conditions, the BPNN always shows an accuracy rate of more than 90%, while the BIC only has a higher accuracy rate when the model order is low and the judgment efficiency of the AIC is below 50%. Finally, the proposed method successfully modeled the stress sequence and obtained the stress change trend. Compared with AIC and BIC, the efficiency of the processing time series is increased by about 50% when an order is obtained by BPNN.

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Safety Prediction Using Vehicle Safety Evaluation Model Passing on Long‐Span Bridge with Fully Connected Neural Network

Cited by 12 publications

References 51 publications

Dynamic Characteristics and Time‐History Analysis of Hydraulic Climbing Formwork for Seismic Motions

Dynamic Characteristics and Time‐History Analysis of Hydraulic Climbing Formwork for Seismic Motions

Feasibility Study of Tractor-Test Vehicle Technique for Practical Structural Condition Assessment of Beam-Like Bridge Deck

Post-Processing of High Formwork Monitoring Data Based on the Back Propagation Neural Networks Model and the Autoregressive—Moving-Average Model

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