• International conference on construction engineering and project management
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• 2011.02a
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• pp.256-261
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• 2011

Labor productivity is a significant factor related to control time, cost, and quality. Many researchers have developed models to define method of measuring the relationship between productivity and various constraints such as the size of working area, maximum working hours, and the crew composition. Most of the previous research has focused on estimating productivity; however, this research concentrates on estimating labor productivity and developing time and cost data for repetitive concrete pouring activity. In Korea, "Standard Estimating" only contains the average productivity data of the construction industry, and it is difficult to predict the time and cost of any particular project; hence, there are some errors in estimating duration and cost for individual activity and project. To address these issues, this research collects data, measures productivity, and develops time and cost data using labor productivity based on field conditions from the collected data. A probabilistic approach is also proposed to develop data. A case study is performed to validate this process using actual data collected from construction sites and it is possible that the result will be used as the EVMS baseline of cost management and schedule management.

• Journal of Construction Engineering and Project Management
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• v.1 no.1
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• pp.37-43
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• 2011

Labor productivity is a significant factor associated with controlling time, cost, and quality. Many researchers have developed models to define methods of measuring the relationship between productivity and various parameters such as the size of working area, maximum working hours, and the crew composition. Most of the previous research has focused on estimating productivity; however, this research concentrates on estimating labor productivity and developing time and cost data for repetitive concrete pouring activity. In Korea, "Standard Estimating" only entails the average productivity data of the construction industry, and it is difficult to predict the time and cost spent on any particular project. As a result, errors occur in estimating duration and cost for individual activities or projects. To address these issues, this research sought to collect data, measure productivity, and develop time and cost data using labor productivity based on field conditions from the collected data. A probabilistic approach is also proposed to develop data. A case study is performed to validate this process using actual data collected from construction sites. It is possible that the result will be used as the EVMS baseline of cost management and schedule management.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.25-31
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• 2009

In this paper, the implementation of a production control system based on RFID has been studied in order to obtain an exact Cost Center data such as the name of workers included a process of work and a time period to finish the process. The cost center of a worker will be correctly obtained by checking the work time using RFID tag data and by transmitting the data to a server of ERP or POP system. And also warming up time, cleaning time, power failure, and out of order sign will be checked and calculated using the data stored in RFID tags attached in workers and machine facilities. Therefore, exact Cost Center data will be obtained by the production control system with touch screens entering the data according to the situation in real time.

• Korean Journal of Construction Engineering and Management
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• v.10 no.3
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• pp.130-138
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• 2009

Bromilow's time-cost (BTC) relationship was examined for building projects in Vietnam using actual construction time and total construction cost. Data set was collected from 77 historical building construction projects completed between 1999 and 2005 which were adjusted by consumer price index (CPI) to 2000 price. Time-cost equations were specified respected to two sectors, public and private, in Vietnamese construction industry and all cases. It is shown that a public funded building project has the longer construction duration than a similar budget private funded project. The resulting models are statistically significant. The adjusted R-square coefficients of all cases, public and private projects models are respectively 0.403, 0.436 and 0.377 mean that the BTC regression lines moderately fit the data set.

• Proceedings of the Safety Management and Science Conference
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• 2004.11a
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• pp.415-438
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• 2004

This dissertation is assumed to continuously occur adjustment cost on present investment. So, I derived from time-nonseparable production-based CAPM and tested the performance of model through data. I also compared time-nonseparable production-based CAPM with time-separable production-based CAPM and CCAPM, CAPM through testifying the performance of model. At the part of applied application, I estimated time-nonseparable PCAPM-betas. The data of Korea consists of 320 listed companies on Korea Stock Exchange (KOSPI) from first quarter 1987 to first quarter 2002. This data also is categorized by scale and industries. Additionally, I estimated time-nonseparable PCAPM-betas through 500 listed companies of New York Stock Exchange (NYSE) from first quarter 1973 to first quarter 2002. I observed the statistical significance of 230 firms by 320 companies in Korea. After that, I compared time-nonseparable PCAPM-betas by firms with time-separable production-based CAPM-betas and CCAPM-betas, CAPM-betas through individual firms. At empirical test, I found that estimated parameter of adjustment cost on time-nonseparable production-based CAPM by scale and industries in Korea had positive value and statistical significance, Moreover, this approach proved to resolve the underestimation of adjustment cost on time-separable production-based CAPM by scale and industries. I also found that the time-nonseparable PCAPM performed better than time-separable production-based CAPM and CCAPM, CAPM. The result from U.S data proved to have similarity to that of Korea. Specifically, I found that time-nonseparable PCAPM-betas by firms performed better than CAPM-betas on individual firms in Korea.

• Journal of Construction Engineering and Project Management
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• v.5 no.1
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• pp.26-31
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• 2015

Success of the construction companies is based on the successful completion of projects within the agreed cost and time limits. Artificial neural networks (ANN) have recently attracted much attention because of their ability to solve the qualitative and quantitative problems faced in the construction industry. For the estimation of cost and duration different ANN models were developed. The database consists of data collected from completed projects. The same data is normalised and used as inputs and targets for developing ANN models. The models are trained, tested and validated using MATLAB R2013a Software. The results obtained are the ANN predicted outputs which are compared with the actual data, from which deviation is calculated. For this purpose, two successfully completed highway road projects are considered. The Nftool (Neural network fitting tool) and Nntool (Neural network/ Data Manager) approaches are used in this study. Using Nftool with trainlm as training function and Nntool with trainbr as the training function, both the Projects A and B have been carried out. Statistical analysis is carried out for the developed models. The application of neural networks when forming a preliminary estimate, would reduce the time and cost of data processing. It helps the contractor to take the decision much easier.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2009.11a
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• pp.253-256
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• 2009

To the success of the megaproject including various and complex facilities, it is needed to establish a database system. Developments in data collection, storage and extracting technology have enabled iPMIS to manage various and complex information about cost and time. Especially, when we consider that both the go and no go decision in feasibility, Cost is an important and clear criteria in megaproject. Thus, Cost data modeling is the basis of the system and is necessary process. This research is focus on the structure and definition about CBS data which is collected from sites. We used four tools which are Function Analysis in VE, Casual loop Diagram in System Dynamics, Decision Tree in Data-mining, and Normalization in SQL to identify its cause and effect relationship on CBS data. Cost data modeling provide iPMIS with helpful guideline.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.114-116
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• 2015

This paper presents a Stochastic Time-Cost Tradeoff analysis system (STCT) that identifies optimal construction methods for activities, hence reducing the project completion time and cost simultaneously. It makes use of schedule information obtained from critical path method (CPM), applies alternative construction methods data obtained from estimators to respective activities, computes an optimal set of genetic algorithm (GA) parameters, executes simulation based GA experiments, and identifies near optimal solution(s). A test case verifies the usability of STCT.

• KIEAE Journal
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• v.16 no.3
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• pp.137-143
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• 2016

Purpose: Apartment housing should conduct a cyclic repair to keep and maintain the building performance since they are constructed. Therefore, the repair plan would be provided for long term period which explains the repair time, items and repair cost. Residents of apartment housing are responsible to pay for the repair activities. For repair cost, residents would reserve the money for repair little by little continuously until the required repair time because the repair cost takes a big burden for residents and lots of money a time. But, there is no systematic approach to provide the long term repair cost because it is no proper forecast of the repair cost to the upcoming repair time. In this study, it aimed at providing the monthly accumulation of the long term repair cost with the survey data in Seoul. Method: For these, the surveyed data are classified into 6 categories and number of data are 1,918. In addition, it developed the repair cost model for the 24 repair works and the cumulation function which is reflected with the each cost model. Result: This study are shown as follows : First, among the various estimation for the repair cost, the power function has a goodness of fit in statistics. Second, the monthly accumulation would be 12,840 won/household in size of $100,000m^2$ management area and $81.7won/m^2$ in size of the 1,000 household number during 40 years.

• Proceedings of the Korea Association of Information Systems Conference
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• 2000.11a
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• pp.36-44
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• 2000

The purpose of this study is to introduce a more efficient forecasting technique, which could help result the reduction of cost in removing the waste of airline in-flight meals. We will use a neural network approach known to many researchers as the “Outstanding Forecasting Technique”. We employed a multi-layer perceptron neural network using a backpropagation algorithm. We also suggested using other related information to improve the forecasting performances of neural networks. We divided the data into three sets, which are training data set, cross validation data set, and test data set. Time lag variables are still employed in our model according to the general view of time series forecasting. We measured the accuracy of our model by “Mean Square Error”(MSE). The suggested model proved most excellent in serving economy class in-flight meals. Forecasting the exact amount of meals needed for each airline could reduce the waste of meals and therefore, lead to the reduction of cost. Better yet, it could enhance the cost competition of each airline, keep the schedules on time, and lead to better service.