• Journal of Construction Engineering and Project Management
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• v.5 no.1
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• pp.11-19
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• 2015

This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project's total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel$^{(R)}$ and EVOLVERTM V.5.5. It provides projects' owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.

• Wind and Structures
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• v.36 no.6
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• pp.423-434
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• 2023

Aerodynamic force coefficients are generally obtained from traditional wind tunnel tests or computational fluid dynamics (CFD). Unfortunately, the techniques mentioned above can sometimes be cumbersome because of the cost involved, such as the computational cost and the use of heavy equipment, to name only two examples. This study proposed to build a deep neural network model to predict the aerodynamic force coefficients based on data collected from CFD simulations to overcome these drawbacks. Therefore, a series of CFD simulations were conducted using different geometric parameters to obtain the aerodynamic force coefficients, validated with wind tunnel tests. The results obtained from CFD simulations were used to create a dataset to train a multilayer perceptron artificial neural network (ANN) model. The models were obtained using three optimization algorithms: scaled conjugate gradient (SCG), Bayesian regularization (BR), and Levenberg-Marquardt algorithms (LM). Furthermore, the performance of each neural network was verified using two performance metrics, including the mean square error and the R-squared coefficient of determination. Finally, the ANN model proved to be highly accurate in predicting the force coefficients of similar bridge sections, thus circumventing the computational burden associated with CFD simulation and the cost of traditional wind tunnel tests.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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• pp.122-123
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• 2018

Recently, CM at Risk delivery system(CM@R) that could solve the problems of Design Bid Build delivery(DBB) system has been emerging. In the CM@R delivery system, the contractor negotiates for a maximum guaranteed price(GMP) with the client at the design stage, and the contractor carries out the construction within the GMP. In CM @ R, the construction company with expertise in construction participates from the design stage to reflects the construction know-how in the design. On the other hand, the modification design frequently occurs due to the change of the construction cost when negotiating the GMP. In addition, uncertainties are inherent in the GMP calculation because the calculation is based on unfinished drawings and documents. This study proposes a probabilistic GMP estimation method applying MCS to the BIM - based cost prediction model, in order to extract the accurate quantity information when estimating the GMP and to cope with the change of the construction cost inherent in uncertainty.

• New & Renewable Energy
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• v.8 no.4
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• pp.3-12
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• 2012

Large offshore wind farms have actively been developed in order to meet the needs for wind energy since the land-based wind farms have almost been fully developed especially in Europe. The key problem for the construction of offshore wind farms may be on the high cost of energy compared to land-based ones. NREL (National Renewable Energy Laboratory) has developed a spreadsheet-based tool to estimate the cost of wind-generated electricity from both land-based and offshore wind turbines. Component formulas for various kinds and scales of wind turbines were made using available field data. In this paper, this NREL estimation model is introduced and applied to the offshore wind turbines now under designing or in production in Korea, and the result is discussed.

• 한국도로학회:학술대회논문집
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• 2010.09a
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• pp.307-310
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• 2010

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.290-295
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• 2004

Along with social changes, school buildings are getting complex and diversified unlike the past. However, objective data analysis on construction costs fall short. In particular, ordering agencies are in a great need of objective and practical construction cost management for on-budget construction and procurement of quality goods. This paper analyzes the design diagram for a newly built school with an order from the Daejeon Metropolitan Office of Education, and compares the analysis with those of other kinds of buildings. The results are: the total construction cost of one school unit is 8,017,596,000 won on average; the cost is in the order of building, machinery and equipment, electricity, communications and civil engineering; as to activity, RC construction takes account of $30.3\%$ of the total construction cost. 1'he cost of school construction per M2 is 838,000 won, which is 6th highest of 11 kinds of constructions and slightly lower than 950,000 won, the average price of comparative constructions. When it comes to the percentage, school building takes mote percentage of the total cost than comparative building while machinery and equipment, electricity and communications takes slightly less percentage. Through simple regression analysis of gross coverage, this paper suggests a model formula with which the total construction cost, construction cost in accordance with activity, how much main construction materials are to be used are predictable.

• Journal of Construction Engineering and Project Management
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• v.4 no.3
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• pp.47-52
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• 2014

In the last decades, Facility Management (FM) has established itself as a key building service factor.FM includes supporting services and organizing functions essential for maintaining, operating and managing physical component and material. The purpose of the paper is to develop an economical analysis for building facilities management during its service life based on limited cost. This method helps to facilities managers and engineers to make better decisions for reducing of facilities assessment costs and increasing the facilities' service life. This paper presents the preliminary development of a model involves three stages process namely data collection, economic computation and economic process optimization. This process was tested for fiberglass doors example in a building interior and exterior system. If executives can manage essential points effectively and make decisions according to a key performance index, cost can be optimized and safety can be enhanced for installation building.

• Journal of Korean Society of Transportation
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• v.34 no.5
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• pp.465-475
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• 2016

Since inaccurate demand estimation for recent urban rail construction may result in financial burden to cities, precise prediction for operating cost as well as construction costs is necessary to avoid or reduce budget loss of the local or central government. The operating cost is directly related to the public fare and affect a policy to determine the rate system. Therefore, there is a pressing need to develop an estimating model for reliable operating cost of urban railway. This study introduces a new model to estimate the operating cost with new variables. It provides a better prediction in accuracy and reliability compared to the existing model, considering the feature of urban railway. For verification of our model, railway operation data from a few cities for the last five years were comprehensively examined to determine variables that affect the operating cost. The operating cost was estimated in a dummy regression model using five independent variables, which were average distance between stations, daily trains distance, total passenger capacity of a train in a train, driving mode(manned/unmanned), and investment type(financial/private).

• Proceedings of the Korean Institute of Building Construction Conference
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• 2017.11a
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• pp.195-196
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• 2017

The number of human accidents in the construction industry is increasing every year, and it constitute the highest percentage among industry. This means that activities performed to prevent safety accidents in the country are not efficient to reduce the rate of accidents in the construction industry. In order to solve this issue, research has been conducted from various perspectives. But, research regarding to quantification model of human accidents is insufficient. the objective of this study is to conduct a basic study on quantification model development of human accidents. To achieve the objective, first, Cause of accident is defined the through literature review. Second, a basic statistic analysis is conducted to determine the characteristics of the accident causes. Third, the analysis is conducted after dividing into four categories : accumulate rate, season, total construction cost, and location. In the future, this study can be used as a reference for developing the safety management checklist for safety management in construction site and development of prediction models of human accident.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1023-1035
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• 2021

The electricity cost of a desalination facility was also predicted and reviewed, which allowed the proposed model to be incorporated into the future design of such facilities. Input data from 2003 to 2014 of the Korea Hydrographic and Oceanographic Agency (KHOA) were used, and the structure of the model was determined using the trial and error method to analyze as well as hyperparameters such as salinity and seawater temperature. The future seawater quality was estimated by optimizing the prediction model based on machine learning. Results indicated that the seawater temperature would be similar to the existing pattern, and salinity showed a gradual decrease in the maximum value from the past measurement data. Therefore, it was reviewed that the electricity cost for seawater desalination decreased by approximately 0.80% and a process configuration was determined to be necessary. This study aimed at establishing a machine-learning-based prediction model to predict future water quality changes, reviewed the impact on the scale of seawater desalination facilities, and suggested alternatives.