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C. Bosch, F. Ballester, C. Otero, M. FernŠndez MarŪn, Coma I., R. Togores, M. A. Pťrez, C. Manchado

Valle: A Roadway Design System In A Virtual Reality Environment

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Full text: content.pdf (770,118 bytes) (available to registered users only)

Series: w78:2006 (browse)
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Charalambos Kyriakou and Symeon E. Christodoulou

Detecting Pavement Patches Utilizing Smartphones Technology and Vehicles

Abstract: Presented herein is a study on the utilization of low-cost technology for detection of roadway pavement anomalies (patches and potholes), by use of sensors on smartphones and of automobiles’ on-board diagnostic (OBD-II) devices for the collection and analysis of vibration-related data while vehicles are in movement. The mobile data collection kit consists of a triaxial accelerometer, a gyroscope and a global positioning sensor. The smartphone-based data collection is complimented with robust regression analysis and a bagged-trees classification model for the classification of pavement anomalies. The proposed system is readily available, low-cost and adequately accurate, and can be utilized in crowd-sourced applications for pavement monitoring. Further, the proposed methodology has been field-tested, exhibiting detection accuracy levels higher than 90% for pavement patches, and it is currently expanded to include larger datasets and a bigger number of pavement defect types.

Keywords: Pavement Anomalies, Detection and Classification, Smartphones Technology, Robust Regression, Bagged Trees

DOI: https://doi.org/10.24928/JC3-2017/0109

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Series: jc3:2017 (browse)
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Georgios Hadjidemetriou and Symeon E. Christodoulou

Pavement Anomalies Detection and Classification Using Entropic Texture Segmentation and Support Vector Machines

Abstract: Presented herein is a vision-based method for the detection of anomalies on roadway pavements, utilizing low-cost video acquisition and image processing of road surface frames collected by a smartphone (or camera) located on a vehicle moving in a real-life urban network, along with entropy-based texture segmentation filters, and support vector machine (SVM) classification. The proposed system, which has been developed in MATLAB, pre-processes video streams for the identification of video frames of changes in image-entropy values, isolates these frames and performs texture segmentation to identify pixel areas of significant changes in entropy values, and then classifies and quantifies these areas using SVMs. The developed SVM is trained and tested by feature vectors generated from the histogram and two texture descriptors of non-overlapped square blocks, which constitute images that includes ‘‘patch’’ and ‘‘no-patch’’ areas. The outcome is composed of block-based and image-based classification, as well as measurement of the patch area.

Keywords: Pavement Condition Evaluation, Road Anomaly Detection, Vision-Based, Entropy, Texture Segmentation

DOI: https://doi.org/10.24928/JC3-2017/0191

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Jianfeng Wan,Tingkan Lu, Wenyan Bi, Zanxu Tei

Research And Development Of Roadway Supporting Database System For Coal-Mine

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Full text: content.pdf (500,022 bytes) (available to registered users only)

Series: w78:2006 (browse)
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Jiwen Zhang, Tim Taylor, Roy Sturgill, Gabe Dadi and Nikiforos Stamatiadis

Predictive Risk Modeling of Differential Bridge Settlement

Abstract: Differential settlement between the roadway pavement resting on embankment fill and the bridge abutment built on more rigid foundation often creates a bump when driving from roadway to bridge, and vice versa. This paper studies the problem at a macroscopic level by determining a method to predict the levels of approach settlement to assist designers in developing remediation plans during project development to minimize the lifecycle costs of bridge bump repairs. A macro method considering a combination of maintenance times, maintenance measures, and observed settlement was used to classify the differential settlement scale as minimal, moderate, and severe. A set of project characteristics including approach, abutment type, embankment, foundation, and traffic volume that may influence the formation of differential settlement were identified and used as parameters to develop a model to predict the settlement severity for a given approach. Logistic regression analyses were implemented to identify the relationships between the levels of differential settlement and the input variables for a sample of 600 randomly selected bridges in Kentucky. Geographic region, approach age, average daily traffic, and the use of approach slabs are identified as the four most predominant factors that can significantly affect the formation of differential settlement. Based on the performance of bridge approaches in Kentucky, how those parameters interacted in the prediction model is illustrated in the logistic regressions.

Keywords: Differential Settlement, Logistic Regression, Prediction Model

DOI: https://doi.org/10.24928/JC3-2017/0060

Full text: content.pdf (338,728 bytes) (available to registered users only)

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Massimiliano Luzi and Igal M. Shohet

Optimal Maintenance of Below-Grade Culverts

Abstract: In Israel, below-grade culverts are critical structure as they are essential points of the water transport network across the country. A lack of maintenance may create safety hazards and economic loss. The current maintenance strategy is that of breakdown maintenance. Such strategy has the drawback of high repair and maintenance costs. This study presents a methodology to determine the optimal maintenance strategy for below-grade culverts during their design life, by evaluating life cycle performances and life cycle costs. A field survey aimed at collecting critical data to develop a suitable deterioration pattern model. An exponential model was found to be the most significant statistically. A Markov chain model was implemented to determine the culvert life cycle performances and life cycle costs. A Maintenance Effectiveness Indicator (MEI) determines the best life cycle maintenance strategy among: breakdown, performance-based, and preventive maintenance. The best maintenance strategy was found to be the preventive. It provides with the highest cost effectiveness and allows for knowing the required budget in advance. Preventive maintenance strategy, which is strongly associated with Lean Construction, permits to save 80% of the resources required under the breakdown maintenance regime. The methodology can be extended to other assets of the roadway infrastructure.

Keywords: Asset Management, Culverts, Markov Chains, Preventive Maintenance

DOI: https://doi.org/10.24928/JC3-2017/0208

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Y. Ji, A. Borrmann, E. Rank, J. Wimmer & W. A. GŁnthner

An Integrated 3D Simulation Framework for Earthwork Processes

Abstract: The paper presents a framework that helps in the planning and analysis of earthwork processes in infrastructure construction projects by applying simulation techniques. In contrast to existing solutions, the framework presented here makes it possible to retrieve all the information required to run the simulation in an automatic way from a holistic 3D model that integrates the 3D roadway model, the 3D surface model and the 3D subsoil model. The most important information needed to simulate earthwork processes are the location and the quantity of cut and fill sections. To generate this data, a computational method has been developed that applies a voxelization to the merged 3D model. The result is a large set of cubes, each of which possesses a dedicated position and material. This mass haul data is much more fine-grained than that used in traditional approaches and forms an excellent basis for performing a detailed earthwork process simulation. The employed simulation engine which is based on the discrete-events paradigm, serves to describe entities such as diggers and trucks, their behaviour (e.g. digger fills truck) and the time required for an atomic process step (to fill a truck, for instance). The result of the simulation is a set of detailed information on the utilization ratio of the employed resources and the time required for completing the entire earthwork project. This allows the user to identify bottlenecks and slack periods and adapt the resources accordingly.

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Full text: content.pdf (247,259 bytes) (available to registered users only)

Series: w78:2009 (browse)
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