Deep learning, more specifically deep convolutional neural networks, is fast becoming a popular choice for computer vision-based automated pavement distress detection. While pavement image analysis has been extensively researched over the past three decades or so, recent ground-breaking achievements of deep learning algorithms in the areas of machine translation, speech recognition, and computer vision has sparked interest in the application of deep learning to automated detection of distresses in pavement images. This paper provides a narrative review of recently published studies in this field, highlighting the current achievements and challenges. A comparison of the deep learning software frameworks, network architecture, hyper-parameters employed by each study, and crack detection performance is provided, which is expected to provide a good foundation for driving further research on this important topic in the context of smart pavement or asset management systems. The review concludes with potential avenues for future research; especially in the application of deep learning to not only detect, but also characterize the type, extent, and severity of distresses from 2D and 3D pavement images.

Various models have been developed over the past several decades to predict the dynamic modulus ∣ E* ∣ of hot-mix asphalt (HMA) based on regression analysis of laboratory measurements. The models most widely used in the asphalt community today are the Witczak 1999 and 2006 predictive models. Although the overall predictive accuracies for these existing models as reported by their developers are quite high, the models generally tend to overemphasize the influence of temperature and understate the influence of other mixture characteristics. Model accuracy also tends to fall off at the low and high temperature extremes. Recently, researchers at Iowa State Univ. have developed a novel approach for predicting HMA ∣ E* ∣ using an artificial neural network (ANN) methodology. This paper discusses the accuracy and robustness of the various predictive models (Witczak 1999 and 2006 and ANN-based models) for estimating the HMA ∣ E* ∣ inputs needed for the new mechanistic-empirical pavement design guide. The ANN-based...

The application of artificial intelligence (AI) techniques to engineering has increased tremendously over the last decade. Support vector machine (SVM) is one efficient AI technique based on statistical learning theory. This paper explores the SVM approach to model the mechanical behavior of hot-mix asphalt (HMA) owing to high degree of complexity and uncertainty inherent in HMA modeling. The dynamic modulus ( | E∗ | ) , among HMA mechanical property parameters, not only is important for HMA pavement design but also in determining HMA pavement performance associated with pavement response. Previously employed approaches for development of the predictive | E∗ | models concentrated on multivariate regression analysis of database. In this paper, SVM-based | E∗ | prediction models were developed using the latest comprehensive | E∗ | database containing 7,400 data points from 346 HMA mixtures. The developed SVM models were compared with the existing multivariate regression-based | E∗ | model as well as the art...

The production and use of biofuel have increased under the context of sustainable development. Biofuel production from plant biomass not only produces biofuel or ethanol but also coproduces products containing lignin, modified lignin, and lignin derivatives. The use of lignin-containing biofuel coproduct in pavement soil stabilization was explored as a new application area. The primary experimental test plan encompassed the comparison of coproduct-treated soils, untreated soils, and traditional stabilizer-treated soils in terms of their strengths. The focus of the secondary experimental test plan was to investigate the effect of additive combinations on strength improvement. The laboratory test matrix in each test plan included variations in additive type, additive content, curing period, and moisture condition. Statistical analyses were performed on unconfined compression strength test results to evaluate whether the strength improvements resulting from the addition of the coproducts to soils are significant. Results indicated that the biofuel coproducts are effective in stabilizing the Iowa Class 10 soil classified as CL or A-6(8). Strengths comparable to traditional additive (fly ash) could be obtained through the use of combined additives (Coproduct A + fly ash; Coproduct A + Coproduct B). The use of biofuel coproduct as a stabilization material for soil appears to be one of many viable answers to the profitability of the biobased products and the bioenergy business.

This paper describes an innovative approach related to the development of a new hot mix asphalt (HMA) dynamic modulus (|E ^*|) prediction model by employing the artificial neural networks (ANNs) methodology. Many studies have been conducted over the last 50 years related to the development of HMA |E ^*| prediction models based on the regression analysis of laboratory measurements. The current study is an attempt to replace the regression analysis with the ANNs that have proved useful for solving certain types of problems that are too complex, poorly understood or resource intensive to tackle using more traditional numerical and statistical methods. The ANN |E ^*| prediction models were developed using the latest comprehensive |E ^*| database that is available to the researchers (from the NCHRP Report 547) containing 7400 data points from 346 HMA mixtures. The ANN model predictions were compared with the 1999 version of the Witczak |E ^*| prediction model, which is included in the mechanistic–empirical pavement design guide (MEPDG) and the new revised version as well. The sensitivity of input variables to the ANN model predictions were also examined and discussed. The ANN |E ^*| models show significantly higher prediction accuracy compared with the existing regression models and could easily be incorporated into the MEPDG. This approach may lead to more accurate characterisation of the HMA dynamic modulus resulting in better performance prediction, thereby reducing the risk of premature pavement failure.