Application of Neural Network Models for Forecasting of Pavement Crack Index and Pavement Condition Rating PDF Download

Author: Jidong Yang
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 284

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Author: Francesco Calabrò
Publisher: Springer Nature
ISBN: 3031068254
Category : Technology & Engineering
Languages : en
Pages : 2873

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Book Description
The book aims to face the challenge of post-COVID-19 dynamics toward green and digital transition, between metropolitan and return to villages’ perspectives. It presents a multi-disciplinary scientific debate on the new frontiers of strategic and spatial planning, economic programs and decision support tools, within the urban–rural areas networks and the metropolitan cities. The book focuses on six topics: inner and marginalized areas local development to re-balance territorial inequalities; knowledge and innovation ecosystem for urban regeneration and resilience; metropolitan cities and territorial dynamics; rules, governance, economy, society; green buildings, post-carbon city and ecosystem services; infrastructures and spatial information systems; cultural heritage: conservation, enhancement and management. In addition, the book hosts a Special Section: Rhegion United Nations 2020-2030. The book will benefit all researchers, practitioners and policymakers interested in the issues applied to metropolitan cities and marginal areas.

Author: Kasthurirangan Gopalakrishnan
Publisher: Springer
ISBN: 3642045863
Category : Computers
Languages : en
Pages : 330

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Book Description
The term “soft computing” applies to variants of and combinations under the four broad categories of evolutionary computing, neural networks, fuzzy logic, and Bayesian statistics. Although each one has its separate strengths, the complem- tary nature of these techniques when used in combination (hybrid) makes them a powerful alternative for solving complex problems where conventional mat- matical methods fail. The use of intelligent and soft computing techniques in the field of geo- chanical and pavement engineering has steadily increased over the past decade owing to their ability to admit approximate reasoning, imprecision, uncertainty and partial truth. Since real-life infrastructure engineering decisions are made in ambiguous environments that require human expertise, the application of soft computing techniques has been an attractive option in pavement and geomecha- cal modeling. The objective of this carefully edited book is to highlight key recent advances made in the application of soft computing techniques in pavement and geo- chanical systems. Soft computing techniques discussed in this book include, but are not limited to: neural networks, evolutionary computing, swarm intelligence, probabilistic modeling, kernel machines, knowledge discovery and data mining, neuro-fuzzy systems and hybrid approaches. Highlighted application areas include infrastructure materials modeling, pavement analysis and design, rapid interpre- tion of nondestructive testing results, porous asphalt concrete distress modeling, model parameter identification, pavement engineering inversion problems, s- grade soils characterization, and backcalculation of pavement layer thickness and moduli.

Author: Inge Hoff
Publisher: CRC Press
ISBN: 100073871X
Category : Technology & Engineering
Languages : en
Pages : 673

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Book Description
Innovations in Road, Railway and Airfield Bearing Capacity – Volume 3 comprises the third part of contributions to the 11th International Conference on Bearing Capacity of Roads, Railways and Airfields (2022). In anticipation of the event, it unveils state-of-the-art information and research on the latest policies, traffic loading measurements, in-situ measurements and condition surveys, functional testing, deflection measurement evaluation, structural performance prediction for pavements and tracks, new construction and rehabilitation design systems, frost affected areas, drainage and environmental effects, reinforcement, traditional and recycled materials, full scale testing and on case histories of road, railways and airfields. This edited work is intended for a global audience of road, railway and airfield engineers, researchers and consultants, as well as building and maintenance companies looking to further upgrade their practices in the field.

Author: Ju Huyan
Publisher:
ISBN:
Category : Machine learning
Languages : en
Pages : 280

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Book Description
Pavement Management System (PMS) analytical tools mainly consist of pavement condition investigation and evaluation tools, pavement condition rating and assessment tools, pavement performance prediction tools, treatment prioritizations and implementation tools. The effectiveness of a PMS highly depends on the efficiency and reliability of its pavement condition evaluation tools. Traditionally, pavement condition investigation and evaluation practices are based on manual distress surveys and performance level assessments, which have been blamed for low efficiency low reliability. Those kinds of manually surveys are labor intensive and unsafe due to proximity to live traffic conditions. Meanwhile, the accuracy can be lower due to the subjective nature of the evaluators. Considering these factors, semiautomated and automated pavement condition evaluation tools had been developed for several years. In current years, it is undoubtable that highly advanced computerized technologies have resulted successful applications in diverse engineering fields. Therefore, these techniques can be successfully incorporated into pavement condition evaluation distress detection, the analytical tools can improve the performance of existing PMSs. Hence, this research aims to bridge the gaps between highly advanced Machine Learning Techniques (MLTs) and the existing analytical tools of current PMSs. The research outputs intend to provide pavement condition evaluation tools that meet the requirement of high efficiency, accuracy, and reliability. To achieve the objectives of this research, six pavement damage condition and performance evaluation methodologies are developed. The roughness condition of pavement surface directly influences the riding quality of the users. International Roughness Index (IRI) is used worldwide by research institutions, pavement condition evaluation and management agencies to evaluate the roughness condition of the pavement. IRI is a time-dependent variable which generally tends to increase with the increase of the pavement service life. In this consideration, a multi-granularity fuzzy time series analysis based IRI prediction model is developed. Meanwhile, Particle Swarm Optimization (PSO) method is used for model optimization to obtain satisfactory IRI prediction results. Historical IRI data extracted from the InfoPave website have been used for training and testing the model. Experiment results proved the effectiveness of this method. Automated pavement condition evaluation tools can provide overall performance indices, which can then be used for treatment planning. The calculations of those performance indices are required for surface distress level and roughness condition evaluations. However, pavement surface roughness conditions are hard to obtain from surface image indicators. With this consideration, an image indicators-based pavement roughness and the overall performance prediction tools are developed. The state-of-the-art machine learning technique, XGBoost, is utilized as the main method in model training, validating and testing. In order to find the dominant image indicators that influence the pavement roughness condition and the overall performance conditions, the comprehensive pavement performance evaluation data collected by ARAN 900 are analyzed. Back Propagation Neural Network (BPNN) is used to develop the performance prediction models. On this basis, the mean important values (MIVs) for each input factor are calculated to evaluate the contributions of the input indicators. It has been observed that indicators of the wheel path cracking have the highest MIVs, which emphasizes the importance of cracking-focused maintenance treatments. The same issue is also found that current automated pavement condition evaluation systems only include the analysis of pavement surface distresses, without considering the structural capacity of the actual pavement. Hence, the structural performance analysis-based pavement performance prediction tools are developed using the Support Vector Machines (SVMs). To guarantee the overall performance of the proposed methodologies, heuristic methods including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are selected to optimize the model. The experiments results show a promising future of machine learning based pavement structural performance prediction. Automated pavement condition analyzers usually detect pavement surface distress through the collected pavement surface images. Then, distress types, severities, quantities, and other parameters are calculated for the overall performance index calculation. Cracks are one of the most important pavement surface distresses that should be quantified. Traditional approaches are less accurate and efficient in locating, counting and quantifying various types of cracks initialed on the pavement surface. An integrated Crack Deep Net (CrackDN) is developed based on deep learning technologies. Through model training, validation and testing, it has proved that CrackDN can detect pavement surface cracks on complex background with high accuracy. Moreover, the combination of box-level pavement crack locating, and pixel-level crack calculation can achieve comprehensive crack analysis. Thereby, more effective maintenance treatments can be assigned. Hence, a methodology regarding pixel-level crack detection which is called CrackU-net, is proposed. CrackU-net is composed of several convolutional, maxpooling, and up-convolutional layers. The model is developed based on the innovations of deep learning-based segmentation. Pavement crack data are collected by multiple devices, including automated pavement condition survey vehicles, smartphones, and action cameras. The proposed CrackU-net is tested on a separate crack image set which has not been used for training the model. The results demonstrate a promising future of use in the PMSs. Finally, the proposed toolboxes are validated through comparative experiments in terms of accuracy (precision, recall, and F-measure) and error levels. The accuracies of all those models are higher than 0.9 and the errors are lower than 0.05. Meanwhile, the findings of this research suggest that the wheel path cracking should be a priority when conducting maintenance activity planning. Benefiting from the highly advanced machine learning technologies, pavement roughness condition and the overall performance levels have a promising future of being predicted by extraction of the image indicators. Moreover, deep learning methods can be utilized to achieve both box-level and pixel-level pavement crack detection with satisfactory performance. Therefore, it is suggested that those state-of-the-art toolboxes be integrated into current PMSs to upgrade their service levels.

Author:
Publisher:
ISBN:
Category : Air travel
Languages : en
Pages : 522

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Author: Zhenyou Lou
Publisher:
ISBN:
Category : Pavements
Languages : en
Pages : 166

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Author: Oladapo Abiola
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
Reliable pavement performance prediction models are essential for pavement design and preservation effort. Pavement performance is defined as the serviceability trend of the pavement over a design period of time. Serviceability indicates the ability of the pavement to serve and sustain the demand of the traffic in the existing condition. Pavement condition can be evaluated in four aspects: roughness, surface distress, structural capacity and skid resistance. In the analysis of the results of the road test conducted by American Association of State Highway Officials (AASHO), the subjective evaluation of serviceability by users was called the Present Serviceability Rating (PSR). The data used in modelling Pavement Serviceability Index (PSI), as reported by some authors, violate the basic assumptions of linear regression modelling in that it does not follow normal distribution. The objective of this study is to explore the relationship between the subjective Pavement Serviceability Rating (PSR) and objective index called Present Serviceability Index for highway sections in South-East, Nigeria. Artificial Neural Network (ANN) model was used to explore the relationship. The method of rating PSR is based on a five point scale: 0-1 (very good); 1-2 (good); 2-3 (fair); 3-4 (critical) and 4-5 (poor). International roughness Index (IRI) was converted to Slope Variance (SV). The input variables are rut depth, cracking, patching and SV. Back-propagation of ANN models with different activation function and number of hidden layers were trained and tested. The dataset was randomly split into three subsets, namely training (60%), testing (20%) and validation (20%) for the ANN model. The optimal models were evaluated with respect to forecasting error and coefficient of determination. Both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for all predictions are plotted. Considering the architecture (4-18-1) with minimum MAE, RMSE and coefficient of determination, the table and figures show that the topology with one hidden layer with hyperbolic transfer function and hyperbolic transfer function for the output layer is the best. Comparison was made with multiple linear regression model which attempts to obtain a relationship between two or more explanatory variables and a response variable by fitting a linear equation to observed data. The results showed that the coefficient of determination for ANN model is 0.90 compared to 0.34 for regression model; ANN has demonstrated its ability to model non-linear data. This result confirms that the input variables are non-linear, and the ANN has shown to forecast with high degree of accuracy over regression analysis.

Author: Adrain Reed Gibby
Publisher:
ISBN:
Category :
Languages : en
Pages : 380

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Author: Eddie Chou
Publisher:
ISBN:
Category : Markov processes
Languages : en
Pages : 222

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Book Description
The primary objectives of this study were to develop models to forecast future pavement conditions and to determine remaining service life of pavements based on the forecasted conditions. Based on available data in the ODOT pavement database, which contains the condition history of each pavement section, along with its location, year of construction, thickness, materials used, climate, and rehabilitation records, individual regression, family regression, and Markov probabilistic models were developed . For the latter two models, pavements were first grouped into "families" with similar characteristics, based on pavement type, priority, District location, and past performance. Forecasting models were then developed for each such "family." The developed models were evaluated by comparing the predicted conditions with the actual observed conditions for the five year period between 2001 and 2005. The Markov model was found to have the highest overall prediction accuracy among all the models evaluated, and it can also predict future distresses in addition to the PCR values. As a result of this study, ODOT can forecast future pavement conditions and estimate the remaining service life of pavements. Future rehabilitation needs can also be determined. Such capabilities will significantly benefit planning and management decision-makings at both project and network levels