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손재호,홍태훈,이상엽 대한토목학회 2013 KSCE JOURNAL OF CIVIL ENGINEERING Vol.17 No.2
Numerous Time-Cost Trade-Off (TCTO) models have been developed to identify the best combination of time and cost in a critical path network. Although there are four relationships in the critical path network, most of the models developed so far have considered only the Finish-Start (F-S) relationship. Thus, an advanced TCTO model that considers all four relationships between activities was developed in this study to accurately present a project, and is presented in this paper. The model also takes into account the lag time between activities. Previous TCTO models minimize the total project cost based on the given crash scenario. Moreover, to enhance the practicality, the model was developed to work with various TCTO scenarios such as continuous, discrete, and even mixed (continuous + discrete). The combined scenario reflects the most realistic situation. Two independent scenarios cannot be combined without mathematical modifications since a rudimentary mixing of two scenarios may provide an incorrect solution. A new formulation technique is introduced to merge the two independent scenarios mathematically and it guarantees the optimal solution.
실용 가능한 배합의 28일 재령 콘크리트 압축강도 예측 기계학습 모델 개발
손재호 한국도로학회 2024 한국도로학회논문집 Vol.26 No.1
PURPOSES : To enhance the accuracy of predicting the compressive strength of practical concrete mixtures, this study aimed to develop a machine learning model by utilizing the most commonly employed curing age, specifically, the 28-day curing period. The training dataset consisted of concrete mixture sample data at this curing age, along with samples subjected to a total load not exceeding 2,350 kg. The objective was to train a machine learning model to create a more practical predictive model suitable for real-world applications. METHODS : Three machine learning models—random forest, gradient boosting, and AdaBoost—were selected. Subsequently, the prepared dataset was used to train the selected models. Model 1 was trained using concrete sample data from the 28th curing day, followed by a comprehensive analysis of the results. For Model 2, training was conducted using data from the 28th day of curing, focusing specifically on instances where the total load was 2,350 kg or less. The results were systematically analyzed to determine the most suitable machine learning model for predicting the compressive strength of concrete. RESULTS : The machine learning model trained on concrete sample data from the 28th day of curing with a total weight of 2,350 kg or less exhibited higher accuracy than the model trained on weight-unrestricted data from the 28th day of curing. The models were evaluated in terms of accuracy, with the gradient boosting, AdaBoost, and random forest models demonstrating high accuracy, in that order. CONCLUSIONS : Machine learning models trained using concrete mix data based on practical and real-world scenarios demonstrated a higher accuracy than models trained on impractical concrete mix data. This case illustrates the significance of not only the quantity but also the quality of the data during the machine learning training process. Excluding outliers from the data appears to result in better accuracy for machine learning models. This underscores the importance of using high-quality and practical mixed concrete data for reliable and accurate model training.
플랜트 설계단계 핵심성공요인(CSF) 도출에 관한 연구
손재호,한충희,김재온,이상엽,Son, Jae-Ho,Han, Choong-Hee,Kim, Jae-On,Lee, Sang-Youb 한국건설관리학회 2007 건설관리 : 한국건설관리학회 학회지 Vol.8 No.6
현재 플랜트 산업이 해외 건설 부문에서 차지하는 비중은 해외 건설 수주액의 60% 이상에 달하고 있다. 하지만 시공부문의 높은 기술경쟁력에 비하여 고부가가치 창출이 가능한 설계부문은 그 기술이 미흡한 실정이다. 따라서 본 연구에서는 플랜트산업의 우수설계능력 확보를 위하여 플랜트 설계단계의 성공에 대한 정의를 내리고, 플랜트 설계업무를 분석한 뒤, 설문조사를 통하여 플랜트 설계단계 프로세스의 업무별 중요도를 파악하였다. 또한 이렇게 파악된 중요도 점수를 공정관리 프로그램에 적용하였고, 이를 바탕으로 제시된 결과에 대한 전문가의 검토를 통하여 최종적으로 플랜트 설계단계의 핵심성공요인(CSF)을 도출하였다. 본 연구의 결과는 플랜트 설계단계 성공의 요인을 파악하고 확인 및 관리를 위한 요소들을 제시하여 설계단계 업무수행의 가이드라인으로 활용될 수 있을 것으로 기대된다. 또한, 향후에는 도출된 핵심성공요인을 이용하여 플랜트의 성공과 실패를 가름할 수 있는 정량적 평가모델 개발 등의 연구가 수행될 수 있을 것이다.