• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1693-1705
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• 2013

To restore old aqueduct in Korea which is a irrigation bridge to supply water in paddy field area, it is needed to estimate approximate costs of restoration because the basic design for estimation of construction costs is often ruled out in current system. In this paper, estimating models of construction costs were developed on the basis of performance data for restoration of RC aqueduct bridges since 2003. The regression analysis (RA) model and case-based reasoning (CBR) model for the estimation of construction costs were developed respectively. Error rate of simple RA model was lower than that of multiple RA model. CBR model using genetic algorithm (GA) has been applied in the estimation of construction costs. In the model three factors like attribute weight, attribute deviation and rank of case similarity were optimized. Especially, error rate of estimated construction costs decreased since limit ranges of the attribute weights were applied. The results showed that error rates between RA model and CBR models were inconsiderable statistically. It is expected that the proposed estimating method of approximate costs of aqueduct restoration will be utilized to support quick decision making in phased rehabilitation project.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.267-273
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• 2023

Algae are an important component of the ecosystem. However, the excessive growth of cyanobacteria has various harmful effects on river environments, and diatoms affect the management of water supply processes. Algal monitoring is essential for sustainable and efficient algae management. In this study, an object detection model was developed that detects and classifies images of four types of harmful cyanobacteria used for the criteria of the algae alert system, and one diatom, Synedra sp.. You Only Look Once(YOLO) v8, the latest version of the YOLO model, was used for the development of the model. The mean average precision (mAP) of the base model was analyzed as 64.4. Five models were created to increase the diversity of the input images used for model training by performing rotation, magnification, and reduction of original images. Changes in model performance were compared according to the composition of the input images. As a result of the analysis, the model that applied rotation, magnification, and reduction showed the best performance with mAP 86.5. The mAP of the model that only used image rotation, combined rotation and magnification, and combined image rotation and reduction were analyzed as 85.3, 82.3, and 83.8, respectively.