• International Journal of Highway Engineering
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• v.3 no.4 s.10
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• pp.105-116
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• 2001

This study was performed to evaluate the characteristics of waste foundry sand (WFS) and the asphalt mixture made of a foundry waste sand. To estimate the applicability of WFS, chemical and physical properties were measured by XRF(X-ray fluorescent), and SEM(Scanning electronic microfilm). To improve the stripping resistance of WFS asphalt mixture, anti-stripping agents (a hydrated lime and a liquid anti-stripping agent) were used. To improve tensile properties and durability of WFS asphalt concrete mixture, LDPE(low-density polyethylene) was used as an asphalt modifier Marshall mix design, indirect tensile strength, tensile strength ratio(TSR) after freezing and thawing, moisture susceptibility and wheel tracking tests were carried out to evaluate performance of WFS asphalt concrete. Comparing with conventional asphalt concrete, WFS asphalt concretes showed similar or the better qualify in mechanical properties, and satisfied all specification limits. Therefore, it Is concluded that waste foundry sand can be recycled as an asphalt pavement material.

• International Journal of Highway Engineering
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• v.17 no.3
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• pp.77-83
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• 2015

PURPOSES : In this study, a fracture-based finite element (FE) model is proposed to evaluate the fracture behavior of fiber-reinforced asphalt (FRA) concrete under various interface conditions. METHODS : A fracture-based FE model was developed to simulate a double-edge notched tension (DENT) test. A cohesive zone model (CZM) and linear viscoelastic model were implemented to model the fracture behavior and viscous behavior of the FRA concrete, respectively. Three models were developed to characterize the behavior of interfacial bonding between the fiber reinforcement and surrounding materials. In the first model, the fracture property of the asphalt concrete was modified to study the effect of fiber reinforcement. In the second model, spring elements were used to simulated the fiber reinforcement. In the third method, bar and spring elements, based on a nonlinear bond-slip model, were used to simulate the fiber reinforcement and interfacial bonding conditions. The performance of the FRA in resisting crack development under various interfacial conditions was evaluated. RESULTS : The elastic modulus of the fibers was not sensitive to the behavior of the FRA in the DENT test before crack initiation. After crack development, the fracture resistance of the FRA was found to have enhanced considerably as the elastic modulus of the fibers increased from 450 MPa to 900 MPa. When the adhesion between the fibers and asphalt concrete was sufficiently high, the fiber reinforcement was effective. It means that the interfacial bonding conditions affect the fracture resistance of the FRA significantly. CONCLUSIONS : The bar/spring element models were more effective in representing the local behavior of the fibers and interfacial bonding than the fracture energy approach. The reinforcement effect is more significant after crack initiation, as the fibers can be pulled out sufficiently. Both the elastic modulus of the fiber reinforcement and the interfacial bonding were significant in controlling crack development in the FRA.

• International Journal of Highway Engineering
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• v.10 no.3
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• pp.179-188
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• 2008

This study was performed as part of the development of the payment adjustment regulations for ensuring high performance of concrete pavements. The objectives of this study were to develop the reasonable quality measurement approaches for the implementation of the payment adjustment regulations and to propose the methods to determine the quality dependent pay factors. First, by using the statistics the slab thickness measurement data was analyzed and the methods to determine the allowable measurement errors, the proper measurement spacing, and the selection of the measurement location were proposed. In addition, to suggest the reasonable methods to determine the pay factors, by using the data of the slab thickness and concrete flexural strength, the pay factors based on the PWL(Percent Within Limits) method used in the USA were compared with those obtained considering the normal probability distribution and t distribution. Finally, the most appropriate method to determine the pay factors was proposed.

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.1
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• pp.44-52
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• 2014

Leakage is an important factor to be considered for the management of underground water supply pipelines in a smart water grid system, especially if the pipelines are aged and buried under the pavement or various structures of a highly populated city. Because the exact detection of the location of such leaks in pipelines is essential for their efficient operation, a new methodology for leak location detection based on frequency band variation, windowing filters, and probability is proposed in this paper. Because the exact detection of the leak location depends on the precision of estimation of time delay between sensor signals due to leak noise, some window functions that offer weightings at significant frequencies are applied for calculating the improved cross-correlation function. Experimental results obtained by applying this methodology to an actual buried water supply pipeline, ~ 253.9 m long and made of cast iron, revealed that the approach of frequency band variation with those windows and probability offers better performance for leak location detection.

• Korean Journal of Construction Engineering and Management
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• v.8 no.5
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• pp.80-91
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• 2007

Crack in Pavements have been continually increased a by water penetration Therefore, the cracks can result in deterioration of the pavements that could be extremely dangerous fro road users. Creak sealing work performed in outdoor is very dangerous, costly and labor intensive. To slove these problems, automated crack sealing systems have been developed. it Would be needed that work information related to crack sealing must be gathered in an effort to used for existing or future crack sealing work. Furthermore, work information related to crack sealing could be utilized in analyzing work productivity and condition. The primary objective of this study is to propose a PDA and Web-based system for work information management of crack sealing which enables to effectively interchange work information between head office and fields, and to accurately collect work information. Finally, it is anticipated that the effective use of the developed PDA and web-based system would be able to effectively share work Information, measure productivity, estimate costs as well as plan future work schedule.

• Journal of the Korean Recycled Construction Resources Institute
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• v.6 no.3
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• pp.199-206
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• 2018

Due to the advantages of less raw materials and fossil fuel consumption, lower carbon footprint, and the capability of pavement performance improvement, the recycling technology of asphalt is developed and applied for road rehabilitation and construction in the western countries over the past two decades. Cold recycled asphalt mixtures are bituminous materials normally made by mixing recycled aggregate from wasted asphalt with an asphalt emulsion and water at room temperature. This paper aims at investigating the properties of cold recycled asphalt mixture with alkali-activated filler according to wasted asphalt aggregate content. As a result, as the content of wasted asphalt aggregate increased, the marshall stability of cold recycled asphalt mixture decreased and void ratio increased. Also, grading curves for cold recycled asphalt mixture as specified in GR criteria were satisfied in all aggregate mixing conditions regardless of the wasted asphalt aggregate content.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.5
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• pp.161-168
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• 2021

Cracks affect the robustness of infrastructures such as buildings, bridge, pavement, and pipelines. This paper presents an automated crack detection system which detect cracks in diverse surfaces. We first constructed the combined crack dataset, consists of multiple crack datasets in diverse domains presented in prior studies. Then, state-of-the-art deep learning models in computer vision tasks including VGG, ResNet, WideResNet, ResNeXt, DenseNet, and EfficientNet, were used to validate the performance of crack detection. We divided the combined dataset into train (80%) and test set (20%) to evaluate the employed models. DenseNet121 showed the highest accuracy at 96.20% with relatively low number of parameters compared to other models. Based on the validation procedures of the advanced deep learning models in crack detection task, we shed light on the cost-effective automated crack detection system which can be applied to different surfaces and structures with low computing resources.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.607-618
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• 2022

This study involves the development of a Hwangto permeable block for rainwater storage tanks. The permeable products that form continuous voids between Hwangto binders and aggregates are fine milled slag powder, which is an industrial by-product generated during the production of Hwangto and iron, and ferro nickel slag. The properties of Hwangto permeable blocks were studied using recycled resource aggregates. The target quality is based on KSF 2394. The Hwangto permeable block for a rainwater storage tank is made of water-permeable material, and the permeability of the Hwangto permeable block itself is 0.1mm/sec or higher, with a physical performance of over 5.0MPa in flexural strength and over 20.0MPa in compressive strength. The physical properties of Hwangto permeable block for rainwater storage tanks were researched and developed. In order to prevent flooding due to heavy rain in summer and the urban heat island phenomenon due to depletion of ground water, continuous pores are formed in the block to secure a permeability function to prevent rainwater from accumulating in the pavement of the floor, and to prevent slippage for comfortable and safe storage.

• Journal of the Korean GEO-environmental Society
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• v.24 no.1
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• pp.37-44
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• 2023

In this study, the performance of the support modular system, as substructure of the proposed sustainable-perpetual modular road system to reduce road construction time and maintenance costs was evaluated. A modular road system consisting of 4 support modular cross-beams with a lower curved surface was constructed on the test-bed. Six load cells and eight LVDTs were installed in the center part of two cross-beam support modular systems. Two loads, 50kN and 100kN, were applied to 15 points on the pavement slab to measure the load and displacement occurring in the modular road system. The measured displacements were less than 1 mm, so it is considered that there was no problem in the stability of the actual road. When comparing the two applied loads and the measured loads in the field test, it was considered that the load transmitted to the ground under the support modular system is very small. It is considered that the modular road system with the support modular system is applicable to the actual road site.

• Advances in materials Research
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• v.13 no.1
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• pp.63-86
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• 2024

Asphalt concrete (AC), is a mixture of bitumen and aggregates, which is very sensitive in the design of flexible pavement. In this study, the Marshall stability of the glass and carbon fiber bituminous concrete was predicted by using Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and M5P Tree machine learning algorithms. To predict the Marshall stability, nine inputs parameters i.e., Bitumen, Glass and Carbon fibers mixed in 100:0, 75:25, 50:50, 25:75, 0:100 percentage (designated as 100GF:0CF, 75GF:25CF, 50GF:50 CF, 25GF:75CF, 0GF:100CF), Bitumen grade (VG), Fiber length (FL), and Fiber diameter (FD) were utilized from the experimental and literary data. Seven statistical indices i.e., coefficient of correlation (CC), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), root relative squared error (RRSE), Scattering index (SI), and BIAS were applied to assess the effectiveness of the developed models. According to the performance evaluation results, Artificial neural network (ANN) was outperforming among other models with CC values as 0.9147 and 0.8648, MAE values as 1.3757 and 1.978, RMSE values as 1.843 and 2.6951, RAE values as 39.88 and 49.31, RRSE values as 40.62 and 50.50, SI values as 0.1379 and 0.2027 and BIAS value as -0.1 290 and -0.2357 in training and testing stage respectively. The Taylor diagram (testing stage) also confirmed that the ANN-based model outperforms the other models. Results of sensitivity analysis showed that the fiber length is the most influential in all nine input parameters whereas the fiber combination of 25GF:75CF was the most effective among all the fiber mixes in Marshall stability.