Performance Prediction Modelling for Flexible Pavement on Low Volume Roads Using Multiple Linear Regression Analysis

Table 1

Brief on the performance prediction models for flexible pavements.

S. number	Authors/model	Dependent variables in the model	Independent variables in the model	Study area	Remarks
1	Hodges et al. (1975)	(i) Roughness; (ii) Cracking	Traffic loading, pavement strength; Traffic loading, pavement strength	Kenya	Data collected from pavements with cement treated bases in 80% of the sample [7]
2	Queiroz and Hudson (1982)	(i) Cracking initiation; (ii) Cracking progression; (iii) Roughness progression	Pavement strength; Age, traffic loading, and pavement strength; Age, traffic loading, and pavement strength	Brazil	The following conditions were not included; (i) Thick bituminous pavements; (ii) Granular base; (iii) Different pavement width [8]
3	George et al. (1989)	(i) Pavement composite condition in 0–100 scale	Age, traffic loading, and pavement strength	Mississippi	Developed for the conditions prevailing in Mississippi state [9]
4	CRRI Model (1994)	(i) Cracking; (ii) Potholing; (iii) Raveling; (iv) Roughness	Traffic loading and pavement strength; Traffic loading and pavement strength; Traffic loading and construction quality; Age, traffic loading, and pavement strength	India	Data collected from National and State Highways in Rajasthan, Gujarat, Uttar Pradesh, and Haryana states [10]
5	Reddy and Veeraragavan (1997)	(i) Rut depth; (ii) Deflection growth; (iii) Crack growth	Age, initial rut, and traffic loading; Age, traffic loading. and initial deflection; Age, traffic loading, and deflection	India	Data collected from National Highways in Andhra Pradesh, Karnataka, Kerala, and Tamil Nadu states in India [11]
6	HDM-4 (2000)	(i) Cracking; (ii) Potholing; (iii) Edge break; (iv) Raveling; (v) Rutting; (vi) Roughness; (vii) Texture depth; (vii) Skid resistance	Traffic loading, pavement strength, and construction quality; Traffic loading, environment, and construction quality; Traffic loading and environmental condition; Age and traffic loading; Traffic loading and pavement strength; Age, traffic loading, pavement strength, and environmental condition; Traffic loading; Age and traffic loading	Romania; India; Thailand; Pakistan; Bangladesh; Brazil; Kosovo; Nepal; Mexico; Vietnam; Morocco; Kyrgyz	(i) Models are complex; (ii) Requires large number of input variables; (iii) Need to be calibrated for local conditions [12]
7	AASHTO model (2001)	Present serviceability; index	Age and traffic loading	USA	(i) Accelerated study; (ii) Study period is only two years; (iii) Little information on long term environmental effect [13]
8	Tare et al. (2013)	Pavement Condition Index (PCI)	Time in year, CVPD, annual rainfall, and; subgrade moisture content (%)	India	Data collected from State and National Highways in Jhabua, Indore, and Dhar districts of Madhya Pradesh in India [14]
9	Prasad et al. (2013)	Roughness	Crack and potholes	Jhunjhunu and Churu districts of Rajasthan, India	Model is limited to village roads in Jhunjhunu and Churu districts only [15]