• KIISE Transactions on Computing Practices
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• v.24 no.2
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• pp.59-66
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• 2018

In this paper, we propose a system to detect various damage automatically inflicted on road pavement by collecting and analyzing data from acceleration and camera sensors in real time. The proposed system sends the collected images, acceleration signals, and GPS coordinates to the road manager and the database in the remote server, shortly after detecting the damage to the road pavement. Our study makes three key contributions. The proposed system 1) enables road managers to maintain road conditions quickly, accurately, and conveniently; 2) allows road mangers to take care of various kinds of damage to the road pavement at the initial stage; and finally 3) even makes it possible to track the damage, which suggests that the integration of a high-level decision support function becomes affordable. We tested the sensitivity and precision of the proposed system against real-time data obtained from the vehicles driving on the highway at an average speed of 100 km/h. With ten iterations, the proposed system achieved an average sensitivity of 74% and an average precision of 84% in road pavement damage detection, which is comparable with the best competing schemes.

• Journal of the Korean GEO-environmental Society
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• v.22 no.10
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• pp.13-19
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• 2021

Cracks in asphalt pavement occur due to changes in weather or impact from vehicles, and if cracks are left unattended, the life of the pavement may be shortened, and various accidents may occur. Therefore, studies have been conducted to detect cracks through images in order to quickly detect cracks in the asphalt pavement automatically and perform maintenance activity. Recent studies adopt machine-learning models for detecting cracks in asphalt road pavement using a Convolutional Neural Network. However, their practical use is limited because they require high-performance computing power. Therefore, this paper proposes a framework for detecting cracks in asphalt road pavement by applying a small deep learning model applicable to mobile devices. The small deep learning model proposed through the case study was compared with general deep learning models, and although it was a model with relatively few parameters, it showed similar performance to general deep learning models. The developed model is expected to be embedded and used in mobile devices or IoT for crack detection in asphalt pavement.

• Ecology and Resilient Infrastructure
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• v.7 no.4
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• pp.274-283
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• 2020

A large amount of fine dust is generated during the process of pavement cutting. Consequently, reducing the amount of fine dust generated during pavement cutting is crucial for the overall management of fine dust. Based on this premise, in this study, a conceptual model is proposed for a device that can be employed for recovering scattered dust generated at the site of pavement cutting. The economic feasibility associated with the incorporation of the proposed model was analyzed. Results obtained from the economic feasibility analysis of the proposed conceptual model indicate that the benefit ratio is 2.96, which is significantly higher than 1. The rate of return is found to be 62.78%, which significantly exceeds the minimum expected rate of return (i.e., 10%), as established on the basis of interviews conducted with companies that implement pavement cutting. Furthermore, the break-even point is found to be at approximately 21.6 months. Hence, the proposed pavement cutter, which features dust suction and a low noise level, is economically feasible.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.532-541
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• 2016

The objective of this paper is to support accurate pavement condition assessment and decision of proper maintenance method and time by conducting visual inspection and calculating the remaining life of pavement from falling weight deflectometer(FWD) data. Each was implemented in the same long-term performance pavement(LTPP) sections. Visual inspection was executed to measure pavement condition indices such as crack, rutting and international roughness index(IRI) and the Seoul Pavement Index(SPI) was calculated based on these results. The dynamic modulus was back-calculated from the FWD data. The remaining pavement lives were determined from equivalent single axle loading(ESAL) and FWD data. Correlation of maintenance priority by each result value was examined. Consequently, the correlation between remaining life to Crack and Rutting was higher than the other factors or indicesbecause IRI is not related to FWD value and SPI value consists with IRI value and other indices. The R-square value of correlation of FWD with Crack and Rutting was 0.65, which indicated an insufficient correlation. Consequently, when decision of maintenance of method, time, etc. is determined, FWD data have to be considered with Crack and Rutting because of those relations.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.376-380
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• 2015

Life-Cycle Cost Analysis (LCCA) for highway projects is known as an effective analytical technique that uses economic principles to evaluate long-term alternative investment options, especially for comparing the values of alternative pavement design structures and construction strategies. In the Unites States, the 2012 Moving Ahead for Progress in the 21^st Century Act (MAP-21) amended the United States Code to mandate that the United States Government Accountability Office (GOA) conducts a study of the best practices for calculating life-cycle costs and benefits for the federally funded highway projects in 2013. The RealCost 2.5CA program was developed and adapted as an official LCCA tool to comply with regulatory requirements for California state highway projects in 2013. Utilization of this California-customized LCCA software helps Caltrans to achieve substantial economic benefits (agency cost and road user cost savings) for highway projects. Proper implementation of LCCA for roadway construction and rehabilitation would deliver noticeable savings of agency's roadway maintenance cost especially in developing counties where financial difficulties exist.

• Korean Journal of Construction Engineering and Management
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• v.21 no.6
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• pp.16-26
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• 2020

Road Pavement Maintenance Work is generated in a variety of different field conditions, and it is difficult to calculate the construction cost because there are no detailed criteria when preparing the estimated construction cost. Unlike new pavements, Road Pavement Maintenance has to be constructed with operating vehicles, and there are many differences in productivity depending on urban areas, limited work hours, night-time, construction area, etc. To compensate for this, the standard for calculation of construction cost provides additional charges for the number of lanes, residential areas, working hours, and night work, but it applied differently depending on construction officials. In this study, construction cost estimation standards that can properly reflect the conditions of the site was investigated for major types of Road Pavement Maintenance work. The site was investigated and analyzed mainly for many construction sites with 'overlay of asphalt after cutting', 'restore surface', 'repair of pavement', and 'recovery of roadway'. The criteria for the application of construction volume separately according to working hours, public places, and land area including extra charges for basic downtown and residential area. The hours of operation were divided into three types(7 hours, 5 hours, 3 hours) excluding movement and preparation time, and each type provided a coefficient for dividing the area of the construction site into five types. The construction cost calculation method based on the construction purpose is site conditions is proposed accordingly, and it is deemed that a plan for the designer to calculate the construction cost has been prepared in consideration of the site conditions.

• Korean Journal of Construction Engineering and Management
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• v.20 no.6
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• pp.34-43
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• 2019

The maintenance cost for road pavement is gradually increasing due to the continuous increase in road extension as well as increase in the number of old routes that have passed the public period. As a result, there is a need for a method of minimizing costs through preventative grievance preventive maintenance requires the establishment of a strategic plan through accurate prediction of road pavement. Hence, In this study, the deep neural network(DNN) and the recurrent neural network(RNN) were used in order to develop the expressway pavement damage prediction model. A superior model among these two network models was then suggested by comparing and analyzing their performance. In order to solve the RNN's vanishing gradient problem, the LSTM (Long short-term memory) circuits which are a more complicated form of the RNN structure were used. The learning result showed that the RMSE value of the RNN-LSTM model was 0.102 which was lower than the RMSE value of the DNN model, indicating that the performance of the RNN-LSTM model was superior. In addition, high accuracy of the RNN-LSTM model was verified through the comparison between the estimated average road pavement condition and the actually measured road pavement condition of the target section over time.

• 한국도로학회:학술대회논문집
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• 1999.11a
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• pp.51-58
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• 1999

• International Journal of Highway Engineering
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• v.2 no.4 s.6
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• pp.111-122
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• 2000

PMS(Pavement Management System) has been developed and/or operated for the freeway and national highway systems in Korea. There is, however, no municipal PMS that is necessary in many cities for efficient management of their roadways. In this study, a municipal Prototype PMS is developed. The Prototype PMS is designed to be operated on PC base. It uses GIS interface for user friendliness that applies color graphics, text and distress pictures to display pavement conditions and rehabilitation information for both current and historical situations. The computer program of the prototype PMS consists of four major functions(inquiry, analysis, data management, and print). Especially, prioritization and M&R(Maintenance and Rehabilitation) alternative selection procedure is designed to be flexible so that the users can modify the decision criteria whenever it is necessary. Database items of the Prototype PMS is carefully selected for the objectives and size of municipal PMS. Reference point method is basically used for the location referencing in the Database. The unit section consists of each loom segment of the road for effective management.

• Journal of Construction Engineering and Project Management
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• v.5 no.2
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• pp.44-52
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• 2015

Various approaches have been used in crack preparations and each of the approaches has advantages and disadvantages. Although the routing method has been widely used and seems to be the best approach among the approaches, it is not a complete solution for crack preparation. This paper compares and evaluates a pneumatic crack cleaning device (CCD) developed by Robotics and Intelligent Construction Automation group at Georgia Tech, over existing devices. Surveys were conducted to discover factors that affect the performance of crack/joint preparation work. Then, data for such information were collected via field tests for devices such as router, heat lancer, air blower and CCD. Performed field test results and follow-up interviews demonstrated that the utilization of CCD has potential to offer improvements in productivity, safety, and maintenance cost. An analytic hierarchical procedure (AHP) and economic analyses were conducted. The AHP analysis considered three factors including safety, quality and productivity while the economic analyses examined the alternatives in various ways. The results indicated that the CCD was ranked first and second for the AHP analysis and economic analysis, respectively. In conclusion, the field tests and results revealed that the utilization of CCD achieved satisfactorily in performance, quality, safety and control, and showed that it has high potential in crack cleaning practice.