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Automated measurement of asphalt pavement rut depth using smartphone imaging

AUTOMATION IN CONSTRUCTION 2025年 174卷

作者： Han, Zhenqiang Tang, Jiaqi Hu, Liqun Jiang, Wei Sha, Aimin Changan Univ Sch Highway Xian 710064 Peoples R China Changan Univ Key Lab Special Area Highway Engn Minist Educ Xian 710064 Shaanxi Peoples R China

Fast and accurate rutting distress detection is essential for driving safety and advancing pavement maintenance automation. However, existing Rut Depth (RD) measurement methods are often inefficient or costly due to complex pavement conditions and expensive equipment. This paper introduces a method to identify and measure RD using smartphone photography and neural networks. Accelerated Pavement Tests (APTs) were conducted to capture rutting evolution, combining measured and photographed data. Grayscale images were analyzed via Fourier transform, identifying the rut-related frequency range (0-0.02 Hz). Grayscale rut curves corresponding to actual rut cross-sections were extracted, and seven feature points were used to calculate grayscale RD. A backpropagation neural network model was trained and validated, demonstrating RD detection within 5-45 mm, with an average absolute error of 1.29 mm. This method provides an alternative for efficient RD measurement in APT and field pavement condition evaluations, offering potentials for improving pavement maintenance practices.

关键词： Road engineering Rut depth measurement Photographic image processing backpropagation neural network model Fourier transform filtering Accelerated pavement test

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Parametric study and optimization of a two-body wave energy converter

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IET RENEWABLE POWER GENERATION 2021年第14期15卷 3319-3330页

作者： Bao, Xingxian Xiao, Weijie Li, Shubo Iglesias, Gregorio China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China China Univ Petr East China Natl Engn Lab Offshore Geophys & Explorat Equipme Qingdao 266580 Peoples R China Univ Coll Cork Environm Res Inst MaREI Coll Rd Cork Ireland Univ Coll Cork Sch Engn Coll Rd Cork Ireland Univ Plymouth Sch Engn Marine Bldg Plymouth PLA 8AA Devon England

The parametric study and optimization of a two-body wave energy converter (WEC) for the wave and current conditions in the region of Zhaitang Island (China) is presented. Nine parameters are considered, and their influence on the power captured by the two-body WEC is investigated following both single-parameter and multi-parameter approaches. A backpropagation neural network model is developed and applied to predict the captured power for any given values of the nine parameters and the wave frequency. Then the robust design method, also known as the Taguchi method, is implemented to study the comprehensive effects of the parameters on the power output of the device. Moreover, scale model experiments are conducted to verify and confirm the influence of the principal parameters on the power output. Combining numerical simulations, a neural network model and experimental work, this study provides an optimization programme for the main parameters of the device in the target sea region and, at a more general level, references for two-body WEC designs based on specific sea states.

关键词： power engineering computing wave frequency captured power two-body WEC backpropagation neural network model two-body wave energy converter Taguchi methods China Zhaitang Island wave power plants principal parameters Taguchi method wave conditions optimisation backpropagation neural nets current conditions single-parameter parametric study robust design method multiparameter approaches power output wave power generation optimization programme

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An Improved Least Square Support Vector Regression Algorithm for Traffic Flow Forecasting

An Improved Least Square Support Vector Regression Algorithm...

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IEEE International Conference on Intelligent Transportation Systems

作者： Wanqiu Lou Yingjie Zhou Peng Sheng Junfeng Wang Coll. of Comput. Sci. Sichuan Univ. Chengdu China

ISBN: (纸本)9781479960798

Due to the nonlinear, non-stationery, complex and stochastic characteristics of short-term traffic flow time series, traditional prediction methods do not work well. This paper presents a short-term traffic flow forecasting model based on the least square support vector regression (LSSVR) algorithm, which is optimized by a glowworm swarm optimization (GSO) algorithm. The GSO algorithm is used to determine two core parameters in the learning process, which significantly influence the predicting performance in the model. An actual example of traffic flow data on one section of highway in Chengdu, China is used to evaluate the performance of the proposed LSS VR-GSO model. The experimental results show that the proposed LSSVR-GSO model has more accurate predicting results than the LSSVR model optimized by the genetic algorithm and the back-propagation neural network model.

关键词： backpropagation least squares approximations neural nets particle swarm optimisation regression analysis road traffic support vector machines traffic engineering computing LSSVR-GSO model backpropagation neural network model genetic algorithm glowworm swarm optimization least square support vector regression algorithm short-term traffic flow forecasting model short-term traffic flow time series traffic flow forecasting Data models Forecasting Genetic algorithms neural networks Prediction algorithms Predictive models Support vector machines Traffic flow forecasting neural network Support Vector network Genetic Algorithm (GA) Particle swarm optimization Prediction algorithms least squares approximations back propagation data models Road traffic Prediction models engineering computing regression analysis neural networks (Computer) Traffic flow

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Temperature Error Compensation for Open-Loop Fiber Optical Gyro using Back-Propagation neural networks with Optimal Structure

Temperature Error Compensation for Open-Loop Fiber Optical G...

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2014中国制导、导航与控制学术会议

作者： Limei Song Wei Zhang AVIC XI''AN Fight Automatic Control Research Institute Xi''an710065 China Delegation of Air ForceXi''an710065 China

ISBN: (纸本)9781479946983

Open-Loop Fiber Optic Gyroscopes (FOG) is widely used,which is easily affected by the temperature around *** temperature model has a very complicated nonlinear characteristic.A BP neural network model with advantage of approximating the nonlinear function was developed to simulate outputs of an open-loop FOG and then compensate the FOG's temperature error in full temperature range??–50??~ +70????.With experimental data,the networks with one-hidden-layer structure adopted the temperature and the temperature change rate as network inputs,and the outputs of FOG as network *** results showed that the number of hidden-layer neurons plays an important role in simulation performance,and the network with 11 hidden-layer neurons offered better precision and ***,the comparison of 4 different training algorithms demonstrated that the Levenberg-Marquardt algorithm resulted in a better convergence during training *** the chosen structure and training algorithm,the BP neural network model was used to compensate the temperature error of the *** was found that the compensated outputs of the FOG became more accurate and more *** addition,the neural network model further proved its superiority of precision and robustness by comparison with a multiple linear regression model and a quadratic curve fitting model.

关键词： backpropagation computerised instrumentation curve fitting error compensation fibre optic gyroscopes neural nets regression analysis BP neural network model FOG Levenberg-Marquardt algorithm backpropagation neural network model hidden-layer neuron multiple linear regression model nonlinear function approximation one-hidden-layer structure open-loop fiber optical gyroscope optimal structure quadratic curve fitting model temperature error compensation training algorithm Biological neural networks Neurons Stability analysis Temperature distribution Temperature measurement Thermal stability Training

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