• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.3
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• pp.551-564
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• 2016

Earthworks are the fundamental steps in a construction job, and are mainly comprised of smaller tasks performed by construction machinery. The productivity of the construction job can be improved by optimizing excavation, filling, and other such operations. Earthworks involve a lot of mechanical work performed by the collaboration between various kinds of construction equipment, which in turn leads to higher fuel consumption. Actual earthworks depend mostly on the intuition and experience of the driver of the machines, thus leading to inefficiency and environmental problems caused by unnecessary emission of carbon, Recently automated and information-oriented technologies are consistently being researched towards the improvement of efficiency of earthworks in the construction industry. The present research involves the introduction and understanding of the decision-making elements of heuristics which can be applied to the earthwork planning. A method is also suggested for creating an effective work path for construction machine to perform task packages (TP) for cutting and filling processes. A simulation test is performed to verify the effectiveness of suggested methods in terms of space interference and total moving distance of construction equipment.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.