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CPSO-XGBoost segmented regression model for asphalt pavement deflection basin area prediction

Science China(Technological Sciences) 2022年第7期65卷 1470-1481页

作者： LI ZhuoXuan SHI XinLi CAO JinDe WANG XuDong HUANG Wei School of Mathematics Southeast UniversityNanjing 210096China School of Cyber Science&Engineering Southeast UniversityNanjing 210096China Yonsei Frontier Lab Yonsei UniversitySeoul 120-749Korea Research Institute of Highway Ministry of TransportBeijing 100088China Intelligent Transportation System Research Center Southeast UniversityNanjing 210096China

The use of non-destructive testing(NDT) equipment, such as the falling weight deflectometer(FWD), provides important estimates of road health and helps to optimize road management regimes. However, periodic road testing and post-processing of the collected data are cumbersome and require much expertise, a considerable amount of time, money, and other resources. This study attempts to develop a reliable prediction method for estimating the deflection basin area of different asphalt pavements using road temperature, load time, and load pressure as main characteristics. The data are obtained from 19 kinds of asphalt pavements on a 2.038-km-long full-scale fleld accelerated pavement testing track named RIOHTrack(Research Institute of Highway Track) in Tongzhou, Beijing. In addition, a chaotic particle swarm algorithm(CPSO) and a segmented regression strategy are proposed in this paper to optimize the XGBoost model. The experiment results of the proposed method are compared with those of classical machine learning algorithms and achieve an average of mean square error and mean absolute error respectively by 5.80 and *** experiments demonstrate the superiority of the XGBoost algorithm over classical machine learning methods in dealing with nonlinear problems in road engineering. Signiflcantly, the method can reduce the frequency of deflection tests without affecting its estimation accuracy, which is a promising alternative way to facilitate the rapid assessment of pavement conditions.

关键词： FWD pavement management systems RIOHTrack deflection basin area CPSO XGBoost segmented regression model

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A Robust segmented Mixed Effect regression model for Baseline Electricity Consumption Forecasting

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 71-80页

作者： Xiaoyang Zhou Yuanqi Gao Weixin Yao Nanpeng Yu Department of Statistics University of CaliforniaRiversideCAUSA Department of Electrical and Computer Engineering University of CaliforniaRiversideUSA

Renewable energy production has been surging around the world in recent *** mitigate the increasing uncertainty and intermittency of the renewable generation,proactive demand response algorithms and programs are proposed and developed to further improve the utilization of load flexibility and increase the efficiency of power system *** of the biggest challenges to efficient control and operation of demand response resources is how to forecast the baseline electricity consumption and estimate the load impact from demand response resources *** this paper,we propose a mixed effect segmented regression model and a new robust estimate for forecasting the baseline electricity consumption in Southern California,USA,by combining the ideas of random effect regression model,segmented regression model,and the least trimmed squares *** the log-likelihood of the considered model is not differentiable at breakpoints,we propose a new backfitting algorithm to estimate the unknown *** estimation performance of the new estimation procedure has been demonstrated with both simulation studies and the real data application for the electric load baseline forecasting in Southern California.

关键词： segmented regression model mixed effects trimmed maximum likelihood demand response electric load

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