An ANN model to correlate roughness and structural performance in asphalt pavements

Sollazzo, Giuseppe; Fwa, T F; Bosurgi, Gaetano

doi:10.1016/j.conbuildmat.2016.12.186

Cited by 107 publications

(36 citation statements)

References 25 publications

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“…Given the advances of deep learning, there has been significant research using these techniques for Pavement Engineering applications [21][22][23][24]. These applications can be assigned to the following areas: Pavement condition and performance predictions [25][26][27][28], Pavement management systems [29][30][31], pavement performance forecasting [32][33][34], structural evaluations [35][36][37], modelling pavement materials [38][39][40] and pavement image analysis and classification [22,[41][42][43][44]. Pavement Image analysis and classification is the most researched area, where the focus has been split between image classifications, where images are classified based on the distress occurring in the image; and object detection, where distresses are located within bounding boxes or masks within the image.…”

Section: The Use Of Deep Learning In Pavement Engineeringmentioning

confidence: 99%

Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning

et al. 2020

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Governments are faced with countless challenges to maintain conditions of road networks. This is due to financial and physical resource deficiencies of road authorities. Therefore, low-cost automated systems are sought after to alleviate these issues and deliver adequate road conditions for citizens. There have been several attempts at creating such systems and integrating them within Pavement management systems. This paper utilizes replicable deep learning techniques to carry out hotspot analyses on urban road networks highlighting important pavement distress types and associated severities. Following this, analyses were performed illustrating how the hotspot analysis can be carried out to continuously monitor the structural health of the pavement network. The methodology is applied to a road network in Sicily, Italy where there are numerous roads in need of rehabilitation and repair. Damage detection models were created which accurately highlight the location and a severity assessment. Harmonized distress categories, based on industry standards, are utilized to create practical workflows. This creates a pipeline for future applications of automated pavement distress classification and a platform for an integrated approach towards optimizing urban pavement management systems.

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Section: The Use Of Deep Learning In Pavement Engineeringmentioning

confidence: 99%

Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning

et al. 2020

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“…Soft computing methods such as neuro-fuzzy models and ANN have been used to predict shear capacity and compressive strength of concrete structures with high accuracy [2,3]. A literature review indicates examples of application of the use of ANNs in a variety of cases for pavement engineering such as for predicting moduli from falling weight deflectometer (FWD) testing [4][5][6], spectral analysis of surface waves [7], prediction of laboratory permeability of hot mix asphalt (HMA) [8] and pavement performance [9][10][11], estimation of laboratory dynamic modulus of HMA [12,13] and pavement temperatures [14], prediction of non-linear material response [15], in-place layer moduli [16], foaming qualities of mixers [17], moisture damage of modified binders [18], field permeability of asphalt pavements [19] and in pavement management [20]. Ceylan et al [21] has summarized the application of ANN in pavement engineering in the following areas: "(1) prediction of pavement condition and performance, (2) pavement management and maintenance strategies, (3) pavement distress forecasting, (4) structural evaluation of pavement systems, (5) pavement image analysis and classification, (6) pavement materials modeling, and (7) other miscellaneous transportation infrastructure applications. "…”

Section: Introductionmentioning

confidence: 99%

A multi-structure multi-run range (MSMRR) approach for using machine learning with constrained data in pavement engineering

Nivedya

Mallick

2020

SN Appl. Sci.

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In pavement engineering, the data sets that are typically obtained from experiments are small and cannot be classified as big data. The effective use of machine learning techniques such as artificial neural networks (ANN) for small data is a challenge because of poor accuracy of models. This paper presents a method of multiple structure multiple run and ranging to optimize ANN to produce models with small data sets with high accuracy. In this method, a large number of data fitting ANNs, with different number of neurons, layers, training and validation ratios, and randomized layer weights and biases are run in parallel, and the most accurate ANN is filtered out on the basis of the lowest MSE or highest R. The process is demonstrated with weather and pavement temperature data for a hot mix asphalt (HMA) and an open graded friction course (OGFC) pavement. Models are generated to predict the temperature at a depth of 12.5 mm below the surface. For the HMA pavement, an accuracy of 99.73% was obtained and an optimum structure was found to be with 4 layers, 11 neurons, 70% training ratio, 15% validation ratio. In the case of the OGFC pavement, an accuracy of 99.75% was obtained for an optimum structure with 3 layers, 11 neurons, 75% training ratio, 15% validation ratio. Furthermore, the fitting/regression problem was converted to a classification problem with different ranges, and then ANNs were utilized to develop very accurate classification models with small datasets.

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“…Meanwhile, this study also briefly discussed the relationships among potholes, map cracking, longitudinal cracking, bleeding, and raveling. Furthermore, Sollazzo et al [19] probed the relationships between roughness and the pavement structural condition with an artificial neural network model, which is built and trained based on a huge number of data from the LTPP program. is model can give many clues for road engineers to determine maintenance measures at the project level, in which the pavement structure strength is very important for selecting maintenance measures.…”

Section: Introductionmentioning

confidence: 99%

Research on Relationships among Different Distress Types of Asphalt Pavements with Semi‐Rigid Bases in China Using Association Rule Mining: A Statistical Point of View

Jing

Liu

Yang

et al. 2019

Advances in Civil Engineering

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Distress types are significant for asphalt pavement maintenance decision, and relationships among them can greatly influence the decision outcomes. In this study, to analyze the relationships among different distress types from a statistical point of view, 282 asphalt pavements with semirigid base structures in 23 regions of China were surveyed to identify 12 distress types, which were subsequently investigated by association rule mining. Results show that the distress types can be categorized into independent distress types (IDDTs), dependent distress types (DDTs), and rutting secondary distress types (RSDTs) based on the relationships. The relationships among IDDTs are the strongest, and those between IDDTs and DDTs, between rutting and RSDTs, and among depression, pumping, and raveling are also strong, while the others are weak. The weak relationships should be ignored, while the strong ones should be considered to reduce cost and guarantee accuracy of maintenance decision. The IDDTs are the most important distress types, so preventing them from occurring or maintaining them immediately can greatly preserve good performance of asphalt pavements. To help analyze the relationships, a distress-type system was established and verified. In conclusion, this work provides new insights to understand distress types.

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An ANN model to correlate roughness and structural performance in asphalt pavements

Cited by 107 publications

References 25 publications

Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning

Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning

A multi-structure multi-run range (MSMRR) approach for using machine learning with constrained data in pavement engineering

Research on Relationships among Different Distress Types of Asphalt Pavements with Semi‐Rigid Bases in China Using Association Rule Mining: A Statistical Point of View

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