Experimentation with Gray Theory for Pavement Smoothness Prediction

Abstract

Riding quality is a dominant characteristic of pavement performance. In the proposed mechanistic–empirical pavement design guide (MEPDG), the functional performance indicator is pavement smoothness as indicated by the international roughness index (IRI). The MEPDG IRI prediction models were developed on the basis of a general hypothesis in which changes in smoothness are caused in part by various distresses, which can be predicted. With pavement distress data from the Long-Term Pavement Performance (LTPP) database, traditional regression analysis was used statistically to establish the MEPDG prediction equations. The gray system theory was devised in the 1980s for modeling uncertain systems with the characteristics of partially known information. A pavement performance prediction system can fit the domain of the gray system. The gray theory–based prediction method was used to develop IRI prediction equations. With the data exported from the LTPP database, it was found that certain specific distresses significantly affect the accuracy of the predictions. Combined with the results of gray relational analysis and the gray prediction methodology, gray model–based smoothness predictions are established by using influencing factors similar to those used in MEPDG. From comparisons of results from the two prediction methodologies with actual LTPP data, it is shown that the gray model–based method provides promising results and is useful for modeling pavement performance.

Pavement Smoothness Prediction Based on Fuzzy and Gray Theories

“…Another observation is that initial IRI is consistently adopted in all FGM models, which demonstrates that Initial IRI always has a significant influence to the deterioration of IRI. This phenomenon is also verified during the development of conventional GM models (Wang and Li, 2007).…”

“…Limitations of using traditional statistical analysis in pavement are discussed by Jiang and Li (2005) and Li et al (2006). In addition, wide variations between actual values and IRI predictions obtained from MEPDG models are observed (Wang and Li, 2007).…”

“…Li et al (2006) presents an initial feasibility study on the use of GT methodologies in pavement smoothness prediction and concluded that the gray models derived better predictions. Using the Long Term Pavement Performance (LTPP) data of General Pavement Study (GPS) sections located in various traffic and environmental conditions, a further study was carried out by Wang and Li (2007) to develop pavement smoothness gray prediction models. Unfortunately, the gray coefficients in the developed models were estimated using the statistical least‐squares method.…”

Tarptautinio keleivių vežimo geležinkeliais proceso kokybės tyrimo daugiatikslis modelis

“…Teisingiems sprendimams priimti technologijos ir valdymo srityse, statybos automatizavimo procesuose, prioritetiniuose geležinkelių projektuose ir kt. yra naudojami ekspertiniai vertinimai (Česnauskis 2007;Zavadskas et al 2005;Wu et al 2008;Abdelrahman et al 2008;Chua, Li 2000;Liu et al 2009;Brauers et al 2008;Малъцев, Захаренков 2007), daugiakriterio vertinimo metodai (Ginevičius et al 2004;Žvirblis, Zinkevičiūtė 2008;Sivilevičius et al 2008;Sivilevičius 2001;Sivilevičius 2002), sprendimų paramos sistemų Topsis ir Saw (Jakimavičius, Burinskienė 2007;Tamošaitienė 2009), lošimų teorijos (Wang et al 2007) ir AHP (Skibniewski et al 1992;Ginevičius, Podvezko 2008;Liu et al 2008;Lai et al 2008;Pan 2008;Podvezko 2009;Ustinovichius et al 2007;Vainiunas et al 2009;Cheng et al 2012) metodai, taikomos rangų procedūros, paremtos statistinių hipotezių tikrinimu (Weed et al 2007). Daugiakriteriai sprendimų priėmimo metodai taip pat taikomi statybos projektų rizikai vertinti (Zavadskas et al 2010), gyvenamosios aplinkos atnaujinimo projektams…”

Asphalt Pavement Roughness Prediction Based on Gray GM(1,1|sin) Model

“…Prediction of IRI had received much attention from researchers. Traditionally, multiple linear regression (MLR) was used to predict IRI [2,3]. The regression model took traffic, material, geometric and climatic condition as model variable.…”