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Hussein Jaafar Khraibani

Assistant professor
Computer Science - Statistics department - Section I - Hadath
Speciality: Computer Science
Specific Speciality: Math-App

Positions
2012 - 2015 : Assistant professor

Lebanese University- Faculty Of Sciences
Hadath

Publications 5 publications
Tristan SENGAKIESSE, Tristan LORINO, Hussein KHRAIBANI Discrete Nonparametric Kernel and Parametric Methods for the Modeling of Pavement Deterioration Communications in Statistics - Theory and Methods (Taylor and Francis) 2014

This article is concerned with one discrete nonparametric kernel and two parametric regression approaches for providing the evolution law of pavement deterioration. The first parametric approach is a survival data analysis method; and the second is a nonlinear mixed-effects model. The nonparametric approach consists of a regression estimator using the discrete associated kernels. Some asymptotic properties of the discrete nonparametric kernel estimator are shown as, in particular, its almost sure consistency. Moreover, two data-driven bandwidth selection methods are also given, with a new theoretical explicit expression of optimal bandwidth provided for this nonparametric estimator. A comparative simulation study is realized with an application of bootstrap methods to a measure of statistical accuracy.

Tristan Lorino, Philippe Lepert, Jean-Marie Marion, Hussein Khraibani Modeling the road degradation process: non-linear mixed effects models for correlation and heteroscedasticity of pavement longitudinal data Transport Research Arena (Elsevier) 2012

Pavement deterioration models are important inputs for the pavement management systems. These models are based on the study of performance data, and they provide the evolution law of pavement deterioration. In order to characterize the pavement deterioration process, several statistical methods have been developed at the French institute of science and technology for transport, development and networks (Ifsttar). This paper presents a nonlinear mixed-effects model accounting for the correlation between repeated observations on the same pavement section. Based on this nonlinear mixed-effects modeling, we investigate and test climatic factor that could explain differences in the parameters between pavement sections.

Khraibani, Z.; Badran, H. M.; Khraibani, H. Records method for the disasters natural Application to the Storm events. Journal of Environmental Science & Engineering 2011

Hussein KHRAIBANI, Tristan LORINO, Philippe LEPERT, Jean-Marie MARION Nonlinear mixed-effects model for the evaluation and prediction of pavement deterioration. Journal of transportation engineering (ASCE) 2010

Pavement deterioration models are important inputs for pavement management systems (PMS). These models are based on the study of performance data, and they provide the evolution law of pavement deterioration. Performance data consist of observations of pavement section conditions, and are collected through several follow-up campaigns on road networks. To characterize the pavement deterioration process, several statistical methods have been developed at the Laboratoire Central des Ponts et Chaussées (LCPC). However, these methods are suboptimal for modeling the evolution of pavement deterioration, as they ignore unit-specific random effects and potential correlation among repeated measurements. This paper presents a nonlinear mixed-effects model enabling accounting for the correlation between observations on the same pavement section. On the basis of this nonlinear mixed-effects modeling, we investigate and identify structural and climatic factors that explain differences in the parameters between pavement sections, and quantify the impact of these factors on pavement evolution. The proposed model provides a good fit for describing the evolution law of different pavement sections. The performance of this model is assessed using simulated and real data.

Hussein KHRAIBANI, Tristan LORINO, Philippe LEPERT, Jean-Marie MARION Comparison of parametric and mixed effect models for the evaluation of pavement deterioration testing results. MAIREPAV06 2009

Pavement deterioration models are important inputs for the pavement management systems. The LCPC has been developed a statical methods to study the evolution law of pavement deterioration. However, these methods are suboptimal for modeling the evolution of pavement deterioration. To this end, we proposed to introduce a logistic mixed effects model (LMM) ollawing to account for the correlation between observations on the same pavement section. A comparison between SDAM and LMM model is carried out. The LMM model provides a good fit to describing the evolution law of different pavement sections.

Languages
Arabic

Native or bilingual proficiency

English

Professional working proficiency

French

Full professional proficiency