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

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 1.59.The 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.

A Robust Segmented Mixed Effect Regression Model for Baseline Electricity Consumption Forecasting

Renewable energy production has been surging around the world in recent years.To 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 operation.One 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 accurately.In 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 estimate.Since the log-likelihood of the considered model is not differentiable at breakpoints,we propose a new backfitting algorithm to estimate the unknown parameters.The 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.

Option-like properties in the distribution of hedge fund returns

Hedge funds have recently become popular because of their low correlation with traditional investments and their ability to generate positive returns with a relatively low volatility.However,a close look at those high-performing hedge funds raises the questions on whether their performance is truly superior and whether the high management fees are justified.Incurring no alpha costs,passive hedge fund replication strategies raise the question on whether they can similarly perform by improving efficiency at reduced costs.Therefore,this study investigates two different model approaches for the equity long/short strategy,where weighted segmented linear regression models are employed and combined with two-state Markov switching models.The main finding proves a short put option structure,i.e.,short equity market volatility,with the put structure present in all market states.We obtain an evidence that the hedge fund managers decrease their short-volatility profile during turbulent markets.