• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.84-92
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• 2021

Care services are a significant asset in human life. Care in its overall nature focuses on human needs and covers several aspects such as health care, homes, personal care, and education. In fact, care deals with many dimensions: physical, psychological, and social interconnections. Very little information is available on estimating the cost of care services that provided to orphans and abandoned children. Prediction of the cost of the care system delivered by governmental or non-governmental organizations to support orphans and abandoned children is increasingly needed. The purpose of this study is to analyze the care cost for orphanage organizations in Saudi Arabia to forecast the cost as well as explore the most influence factor on the cost. By using business analytic process that applied statistical and machine learning techniques, we proposed a model includes simple linear regression, Naive Bayes classifier, and Random Forest algorithms. The finding of our predictive model shows that Naive Bayes has addressed the highest accuracy equals to 87% in predicting the total care cost. Our model offers predictive approach in the perspective of business analytics.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.349-352
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• 2015

Size of building has a direct relationship with building cost, energy use and space maintenance cost. Therefore, minimizing building size during a project development is of paramount importance against such wastes. However, incautious reduction of building size may result in crowded space, and therefore harms the functionality despite the fact that building is supposed to satisfactorily support users' activity. A well-balanced design solution is, therefore, needed at an optimum level that minimizes building size in tandem with providing sufficient space to maintain functionality. For such design, architects and engineers need to be informed accurate and reliable space-use information. We present in this paper a conceptual framework of an agent-based space-use prediction simulation system that provides individual level space-use information over time in a building in consideration of project specific user information and activity schedules, space preference, ad beavioural rules. The information will accordingly assist architects and engineers to optimize space of the building as appropriate.

• Korean Journal of Construction Engineering and Management
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• v.14 no.3
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• pp.42-52
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• 2013

The aim of this study is to present a prediction model of construction cost for a bridge that has a high reliability using historical data from the planning phase based on a CBR (Case-Based Reasoning) method in order to overcome limitations of existing construction cost prediction methods, which is linearly estimated. To do this, a reasoning model of bridge construction cost by a spreadsheet template was suggested using complexly both CBR and GA (Genetic Algorithm). Besides, this study performed a case study to verify the suggested cost reasoning model for bridge construction projects. Measuring efficiency for a result of the case study was 8.69% on average. Since accuracy of the suggested prediction cost is relatively high compared to the other analysis methods for a prediction of construction cost, reliability of the suggested model was secured. In the case that information for detailed specifications of each bridge type in an initial design phase is difficult to be collected, the suggested model is able to predict the bridge construction cost within the minimized measuring efficiency with only the representative specifications for bridges as an improved correction method. Therefore, it is expected that the model will be used to estimate a reasonable construction cost for a bridge project.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.137-142
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• 2006

Cost and duration is important factors which directly affect profit therefore must be forecasted correctly to accomplish success of projects. So construction company uses EVMS(Earned Value Management System) to forecast final cost and duration. But previous forecasting model has low accuracy because of its linear forecasting method and can't reflect characteristic of company and project and changes as each progress. This paper presents cost and duration forecasting model using the slope prediction of cost and duration as each progress to reflect the various characteristics of construction industry. EVMS data of 23 road construction projects was used to make up regression analysis equation of slope forecasting model.

• Journal of the Korea Institute of Building Construction
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• v.22 no.3
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• pp.317-326
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• 2022

With the development of various technologies in the area of artificial intelligence, a number of studies to application of artificial intelligence technology in the construction field are underway. Diverse technologies have been applied to the task of predicting construction costs, and construction cost prediction technologies applying artificial intelligence technologies have recently been developed. However, it is difficult to secure the vast amount of construction cost data required for machine learning, which has not yet been practically used. In this study, to predict the construction cost, the latest artificial neural network(ANN) method is used to propose a method to improve the construction cost prediction performance. In particular, to improve predictive performance, a log conversion method of target variables and a feature scaling method to eliminate the difference in the relative influence of each column data are applied, and their performance in predicting construction cost is compared and analyzed.

• ETRI Journal
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• v.38 no.5
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• pp.807-817
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• 2016

A novel fast algorithm is suggested for a coding unit (CU) mode decision using pseudo rate-distortion based on a separated encoding structure in High Efficiency Video Coding (HEVC). A conventional HEVC encoder requires a large computational time for a CU mode prediction because prediction and transformation procedures are applied to obtain a rate-distortion cost. Hence, for the practical application of HEVC encoding, it is necessary to significantly reduce the computational time of CU mode prediction. As described in this paper, under the proposed separated encoder structure, it is possible to decide the CU prediction mode without a full processing of the prediction and transformation to obtain a rate-distortion cost based on a suitable condition. Furthermore, to construct a suitable condition to improve the encoding speed, we employ a pseudo rate-distortion estimation based on a Hadamard transformation and a simple quantization. The experimental results show that the proposed method achieves a 38.68% reduction in the total encoding time with a similar coding performance to that of the HEVC reference model.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.11a
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• pp.153-154
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• 2012

The accuracy of cost estimation at an early stage in school building project is one of the critical factors for successful completion. So many method and techniques have developed that can estimate construction cost using limited information available in the early stage. Among the techniques, Support Vector Machine(SVM) has received attention in various field due to its excellent capacity for self-learning and generalization performance. Therefore, the purpose of this study is to verify the applicability of cost prediction model based on SVM in school building project at the early stage. Data used in this study are 139 school building cost constructed from 2004 to 2007 in Gyeonggi-Do. And prediction error rate of 7.48% in support vector machine is obtained. So the results showed applicability of using SVM model for predicting construction cost of school building projects.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.510-515
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• 2011

Urban renewal projects, in the form of mega-projects, are being actively implemented both nationally and internationally to revitalize inactive cities. These programs, however, are difficult to manage efficiently due to their need for a large budget over a long period of time and due to conflicts with stockholders. Moreover, existing cost and duration management systems are structured with emphasis on the design and construction stage of unit projects, thus limiting their application to long-term mega-projects that are integrated with various facilities. To solve these problems, this study developed a web-based system that can collectively manage the cost and duration of mega-projects at a program level. The unit modules included in the system--CBS organization, construction cost and duration prediction, and total cost and duration prediction--can support decision-making at the early stage of the program. Furthermore, the modules, which include contract management, execution management, change management, and program progress management, support the program operations for its successful accomplishment. The web-based cost and duration management system developed in this study is expected to be used as a valuable tool that supports the successful accomplishment of mega-projects through their efficient management throughout their life cycle.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.653-661
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• 2007

Overseas construction projects tend to be more complex than domestic projects, being exposed to more external risks, such as politics, economy, society, and culture, as well as more internal risks from the project itself. It is crucial to have an early understanding of the project condition, in order to be well prepared in various phases of the project. This study compares a structural equation model and multiple regression analysis, in their capacity to predict cost performance of international construction projects. The structural equation model shows a more accurate prediction of cost performance than does regression analysis, due to its intrinsic capability of considering various cost factors in a systematic way.

• Proceedings of the KSR Conference
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• 2002.10a
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• pp.376-381
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• 2002

In this study, we have analyzed the cost of korea high-speed rail project. The predicted cost in planning phase and adjustment data to 5th year are collected. Then, predicted cost is compared with adjustment in year/item/system base. We make a project history table for criteria to review project history and research ＆ development activity. We have developed CBS(cost breakdown structure) and allocated adjustment data to them. It is shown that cost prediction related to research St development activity in planning phase is relatively correct.