Pavement diagnosis accuracy with controlled application of artificial neural network

Abstract

Results of research studies, the amount of input data available in pavement management system databases, and artificial intelligence methods serve as versatile tools, well-suited for the analysis conducted as a part of pavement management system. The key source of new and to be employed knowledge is provided. In terms of e.g. assessing thickness of bituminous pavement layers, the default solution is pavement drilling, but for the purposes of pavement management it is prohibitively expensive. This paper attempts to test the original concept of employing an empirical relationship in an algorithm verifying results produced by the artificial neural network method. The assumed multistage asphalt pavement layer thickness identification control process boils down to evaluating test results of the road section built using both, reinforced and non-reinforced pavement structure. By default, the artificial neural network training set has not included the reinforced pavement sections. Hence, it has been possible to identify “perturbations” in assumptions underlying the training set. Pavement test section points’ results are indicated in the automated manner, which, in line with implemented methods, is not generated by perturbations caused by divergence between actual pavement structure and assumptions taken for purposes of building pavement management system database, and the artificial neural network learning dataset is based on.

Prediction of Transportation Costs Using Trapezoidal Neutrosophic Fuzzy Analytic Hierarchy Process and Artificial Neural Networks

“…Definition-2: Assume a universe of discourse is X. A single-valued neutrosophic set β over X taking the form as in [62] Author's name Subject area Zhengping et al [47] Driver behavior Kikuchi et al [48] Origin-destination parameter estimation Pozarycki et al [49] Pavement maintenance Bielli et al [50] Traffic Control Bhattacharya [51] Driver behavior Kayikci [52] Freight logistics Saâdaoui [53] Air traffic management George et al [54] Vehicle classification Lin et al [55] Travel time in transportation forecasting Mazarakis and Avaritsiotis [56] Vehicle classification Kirby and Parker [36] Traffic pattern analysis Swiderski et al [57] Freight operations Ghanim and Lebdeh [58] Traffic forecasting Salido et al [59] Maritime transport Lu et al [60] Traffic control Xiao et al [61] Air transport This study Transportation cost forecasting sophic numbers β denoted by (a 1 , a 2 , a 3 , a 4 ), T β , I β , F β are neutrosophic set in R with the truth membership, indeterminacy membership and falsity membership functions are defined below:…”

Diversified gradient boosting ensembles for prediction of the cost of forwarding contracts

“…In the recent years, the technologies of data mining and machine learning have been applied to investigate a range of issues in international freight transportation, supply chain and logistics management, e.g. driver behavior analysis [2], [3], origindestination parameter estimation [4], pavement maintenance [5], traffic control and forecasting [6], [7], [8], freight logistics [9], air traffic management [10], vehicle classification [11], travel time prediction [12], traffic pattern analysis [13], freight demand prediction [14], traffic volume forecasting [15], transportation cost forecasting [16], etc. A good literature review regarding utilizing machine learning on freight transportation and logistics applications has been published in [18].…”

Optimizing Artificial Neural Networks For The Evaluation Of Asphalt Pavement Structural Performance

“…However, the problem complexity and the lack of knowledge in some of the physical and mechanical relationships among the involved processes suggest the adoption of numerical and advanced "machine learning" approaches -and, in particular, Artificial Neural Networks (ANNs) -for solving the issue (Adeli, 2001). As it is known, ANNs have been widely applied in different areas of civil engineering (for example, structural, construction, environmental, geotechnical and infrastructure engineering) with positive results (Bosurgi & Trifirò, 2005;Bosurgi, D'Andrea, & Pellegrino, 2013;Ceylan, Bayrak, & Gopalakrishnan, 2014;Fwa & Chan, 1993;He, Qi, Hang, Zhao, & King, 2014;Pozarycki, 2015;Roberts & Attoh-Okine, 1998;Terzi, 2007).…”