VALIDITY OF GARBER MODEL IN PREDICTING PAVEMENT CONDITION INDEX OF FLEXIBLE PAVEMENT IN KERBALA CITY

Pavement Condition Index (PCI) is one of the important basics in pavement maintenance management system (PMMS), and it is used to evaluate the current and future pavement condition. This importantance in decision making to limit the maintenance needs, types of treatment, and maintenance priority. The aim of this research is to estimate the PCI value for flexible pavement urban roads in the study area (kerbala city) by using Garber et al. developed model. Based on previous researches, data are collected for variables that have a significant impact on pavement condition. Data for pavement age (AGE), average daily traffic (ADT), and structural number (SN) were collected for 44 sections in the network roads. A field survey (destructive test (core test) and laboratory test (Marshall Test)) were used to determine the capacity of structure layer of pavement (SN). The condition index (CI) output from a developed model was compared with the PCI output of PAVER 6.5.7 by using statistical analysis test. The developed model overestimates value of CI rather than PCI estimated from PAVER 6.5.7 due to statistical test to a 95% degree of confidence, (R = 0.771) for 44 sections (arterial and collector)

Efficient Feature Extraction Algorithms to Develop an Arabic Speech Recognition System

This paper studies three feature extraction methods, Mel-Frequency Cepstral Coefficients (MFCC), Power-Normalized Cepstral Coefficients (PNCC), and Modified Group Delay Function (ModGDF) for the development of an Automated Speech Recognition System (ASR) in Arabic. The Support Vector Machine (SVM) algorithm processed the obtained features. These feature extraction algorithms extract speech or voice characteristics and process the group delay functionality calculated straight from the voice signal. These algorithms were deployed to extract audio forms from Arabic speakers. PNCC provided the best recognition results in Arabic speech in comparison with the other methods. Simulation results showed that PNCC and ModGDF were more accurate than MFCC in Arabic speech recognition.</jats:p