• Journal of the Korean Geotechnical Society
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• v.22 no.6
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• pp.15-26
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• 2006

Although numerous investigations have been performed over the years to predict the behavior and bearing capacity of piles, the mechanisms are not yet entirely understood. The prediction of bearing capacity is a difficult task, because large numbers of factors affect the capacity and also have complex relationship one another. Therefore, it is extremely difficult to search the essential factors among many factors, which are related with ground condition, pile type, driving condition and others, and then appropriately consider complicated relationship among the searched factors. The present paper describes the application of Artificial Neural Network (ANN) in predicting the capacity including its components at the tip and along the shaft from dynamic load test of the driven piles. Firstly, the effect of each factor on the value of bearing capacity is investigated on the basis of sensitivity analysis using ANN modeling. Secondly, the authors use the design methodology composed of ANN and genetic algorithm (GA) to find optimal neural network model to predict the bearing capacity. The authors allow this methodology to find the appropriate combination of input parameters, the number of hidden units and the transfer structure among the input, the hidden and the out layers. The results of this study indicate that the neural network model serves as a reliable and simple predictive tool for the bearing capacity of driven piles.

• Safety and Health at Work
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• v.14 no.4
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• pp.457-466
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• 2023

Background: During hot environment work tasks with whole-body enclosed anti-bioaerosol suit, the combined effect of heavy sweating and exhaled hot humid air may cause the N95 medical respirator to saturate with water/sweat (i.e., water-blocking). Methods: 32 young male subjects with different body mass indexes (BMI) in whole-body protection (N95 medical respirator + one-piece protective suit + head covering + protective face screen + gloves + shoe covers) were asked to simulate waste collecting from each isolated room in a seven-story building at 27-28℃, and the weight, inhalation resistance (R_f), and aerosol penetration of the respirator before worn and after water-blocking were analyzed. Results: All subjects reported water-blocking asphyxia of the N95 respirators within 36-67 min of the task. When water-blocking occurred, the R_f and 10-200 nm total aerosol penetration (P_t) of the respirators reached up to 1270-1810 Pa and 17.3-23.3%, respectively, which were 10 and 8 times of that before wearing. The most penetration particle size of the respirators increased from 49-65 nm before worn to 115-154 nm under water-blocking condition, and the corresponding maximum size-dependent aerosol penetration increased from 2.5-3.5% to 20-27%. With the increase of BMI, the water-blocking occurrence time firstly increased then reduced, while the R_f, P_t, and absorbed water all increased significantly. Conclusions: This study reveals respirator water-blocking and its serious negative impacts on respiratory protection. When performing moderate-to-high-load tasks with whole-body protection in a hot environment, it is recommended that respirator be replaced with a new one at least every hour to avoid water-blocking asphyxia.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.3
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• pp.147-153
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• 2013

There exist various types of the WEC (Wave Energy Converter), and among them, the point absorber is the most popularly investigated type. However, it is difficult to find examples of systematically measured data analysis for the design of the point absorber type of power buoy in the world. The study investigates the wave load acting on the point absorber type resonance power buoy wave energy extraction system proposed by Kweon et al. (2010). This study analyzes the time series spectra with respect to the three-year wave data (2002.05.01~2005.03.29) measured using the pressure type wave gage at the seaside of north breakwater of Hupo harbor located in the east coast of the Korean peninsula. From the analysis results, it could be deduced that monthly wave period and wave height variations were apparent and that monthly wave powers were unevenly distributed annually. The average wave steepness of the usual wave was 0.01, lower than that of the wind wave range of 0.02-0.04. The mode of the average wave period has the value of 5.31 sec, while mode of the wave height of the applicable period has the value of 0.29 m. The occurrence probability of the peak period is a bi-modal type, with a mode value between 4.47 sec and 6.78 sec. The design wave period can be selected from the above four values of 0.01, 5.31, 4.47, 6.78. About 95% of measured wave heights are below 1 m. Through this study, it was found that a resonance power buoy system is necessary in coastal areas with low wave energy and that the optimal design for overcoming the uneven monthly distribution of wave power is a major task in the development of a WEF (Wave Energy Farm). Finding it impossible to express the average spectrum of the usual wave in terms of the standard spectrum equation, this study proposes a new spectrum equation with three parameters, with which basic data for the prediction of the power production using wave power buoy and the fatigue analysis of the system can be given.