Investigation and modelling of asphalt pavement performance in cold regions

Zeiada, Waleed; Hamad, Khaled; Omar, Maher; Underwood, B. Shane; Khalil, Mohamad Ali; Karzad, Abdul Saboor

doi:10.1080/10298436.2017.1373391

Cited by 39 publications

(12 citation statements)

References 19 publications

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“…The selection of features leads to the identification of optimal features. Feature selection decreases dimensionality by eliminating redundant or excessive data to fit the best model ( 32 ). Feature selection is needed to enhance the computational efficiency and pattern recognition of machine-learning algorithms.…”

Section: Methodsmentioning

confidence: 99%

Novel Soft-Computing Approach to Better Predict Flexible Pavement Roughness

Naseri

Shokoohi

Jahanbakhsh

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Road infrastructures are fundamental parts of peoples’ lives, allowing them to access various destinations and activities. Accordingly, infrastructure should be in an appropriate condition. A pavement maintenance plan should be optimized, and pavement condition should be predicted accurately to obtain optimal pavement maintenance solutions. Therefore, the prediction of pavement conditions with high accuracy has been an immense concern. This study aims to introduce a new approach to accurately predict pavement international roughness index (IRI) over the long term. To this end, all the vital parameters, including initial IRI, pavement age, lane width, traffic loadings, structural characteristics, climatic features, and pavement distresses, are considered. With all the vital parameters, the prediction problem includes 58 variables. Thus, the application of a proper feature-selection technique is vital. To this end, a novel hybrid feature-selection method is introduced by a combination of arithmetic optimization algorithm and stochastic gradient descent regression (AOA-SGDR). Moreover, the performance of the proposed feature-selection method is compared with Lasso and all features. Five machine-learning algorithms, including random forest regression (RFR), support vector machine, multi-layer perceptron, decision-tree regression, and multiple linear regression, are employed for the prediction process. By employing AOA-SGDR, the average testing-data mean absolute error (MAE) reduces by at least 7.92%. Meanwhile, RFR provides the highest accuracy, with average testing-data MAE of 0.095 m/km. Moreover, analyzing the parameters indicates that initial IRI, pavement age, equivalent single axle load (ESAL), and structural number (SN) have the most significant relative influence on IRI.

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Section: Methodsmentioning

confidence: 99%

Novel Soft-Computing Approach to Better Predict Flexible Pavement Roughness

Naseri

Shokoohi

Jahanbakhsh

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…In Equation (8), n is the number of samples, IRI act and IRI pred are the actual and the predicted IRI value, respectively, IRI act is the average value of the actual IRI. The range of R 2 values 0-1, with 1 being the highest precise relationship possible.…”

Section: Model Assessment Criteriamentioning

confidence: 99%

“…IRI is generally expressed in meters per kilometer or inches per mile [7]. At present, due to its stability over time and transferability over the world, IRI is utilized by many highway agencies worldwide as a sound and practical index for measuring ride quality and enables the identification of MR activities [8,9].…”

Section: Introductionmentioning

confidence: 99%

Application of Adaptive Neuro–Fuzzy Inference System for Forecasting Pavement Roughness in Laos

et al. 2022

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Laos Pavement Management System (PMS) manages 7700 km of National Roads (NRs) and estimates their Maintenance and Rehabilitation (MR) needs based on assessing pavement roughness conditions. This research aims to develop two International Roughness Index (IRI) models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections using Adaptive Neuro-Fuzzy Inference System (ANFIS). A historical database of 14 years was employed for predicting the IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The optimum ANFIS structure comprises a hybrid learning algorithm with six fuzzy rules of generalized bell curve membership functions (Gbellmf) for the DBST model and nine fuzzy rules of two-sided Gaussian membership functions (Gauss2mf) for the AC model. Both models used the constant membership function for the output variable (IRI). The statistical evaluation results revealed that both ANFIS models (DBST and AC) have a good prediction capacity with high values of coefficient of determination (R2 0.93 and 0.88) and low values of Mean Absolute Error (MAE 0.28 and 0.27) and Root Mean Squared Percentage Error (RMSPE 7.03 and 9.98). In addition, results revealed that ANFIS models yielded higher prediction accuracy than Multiple Linear Regression (MLR) models previously developed under the same conditions.

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“…There are several characteristics that can be assessed in pavements, but are usually classified in surface characteristics (including longitudinal profile, roughness, and surface texture and skid resistance), pavement distresses, structural evaluation, and sub-surface characteristics. However, there is not a universal approach and each highway administration collects pavement condition data following its own criteria [5,6]. Moreover, there are various indices for measuring the same characteristic [7,8].…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Cracks in Asphalt Pavement Using Deep Learning to Overcome Weaknesses in Images and GIS Visualization

2021

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The crack ratio is one of the indices used to quantitatively evaluate the soundness of asphalt pavement. However, since the inspection of pavement requires much labor and cost, automatic inspection of pavement damage by image analysis is required in order to reduce the burden of such work. In this study, a system was constructed that automatically detects and evaluates cracks from images of pavement using a convolutional neural network, a kind of deep learning. The most novel aspect of this study is that the accuracy was recursively improved through retraining the convolutional neural network (CNN) by collecting images which had previously been incorrectly analyzed. Then, study and implementation were conducted of a system for plotting the results in a GIS. In addition, an experiment was carried out applying this system to images actually taken from an MMS (mobile mapping system), and this confirmed that the system had high crack evaluation performance.

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Investigation and modelling of asphalt pavement performance in cold regions

Cited by 39 publications

References 19 publications

Novel Soft-Computing Approach to Better Predict Flexible Pavement Roughness

Novel Soft-Computing Approach to Better Predict Flexible Pavement Roughness

Application of Adaptive Neuro–Fuzzy Inference System for Forecasting Pavement Roughness in Laos

Automatic Detection of Cracks in Asphalt Pavement Using Deep Learning to Overcome Weaknesses in Images and GIS Visualization

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