• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.159-168
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• 2015

Aggregate Production Planning determines levels of production, human resources, inventory to maximize company's profits and fulfill customer's demands based on demand forecasts. Since performance of aggregate production planning heavily depends on accuracy of given forecasting demands, choosing an accurate forecasting method should be antecedent for achieving a good aggregate production planning. Generally, typical forecasting error metrics such as MSE (Mean Squared Error), MAD (Mean Absolute Deviation), MAPE (Mean Absolute Percentage Error), and CFE (Cumulated Forecast Error) are utilized to choose a proper forecasting method for an aggregate production planning. However, these metrics are designed only to measure a difference between real and forecast demands and they are not able to consider any results such as increasing cost or decreasing profit caused by forecasting error. Consequently, the traditional metrics fail to give enough explanation to select a good forecasting method in aggregate production planning. To overcome this limitation of typical metrics for forecasting method this study suggests a new metric, WACFE (Weighted Absolute and Cumulative Forecast Error), to evaluate forecasting methods. Basically, the WACFE is designed to consider not only forecasting errors but also costs which the errors might cause in for Aggregate Production Planning. The WACFE is a product sum of cumulative forecasting error and weight factors for backorder and inventory costs. We demonstrate the effectiveness of the proposed metric by conducting intensive experiments with demand data sets from M3-competition. Finally, we showed that the WACFE provides a higher correlation with the total cost than other metrics and, consequently, is a better performance in selection of forecasting methods for aggregate production planning.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.72-76
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• 2015

Capital project benchmarking requires an effective cost normalization process to compare cost performance of projects accomplished in different time and location. Existing cost normalization approaches have been established based on the assumption that all required information for cost normalization is fully identified once a project is completed. Cost normalization, however, is sometimes required to evaluate phase-level outcomes of an ongoing project where the required information is not fully available. This paper aims to provide a cost normalization procedure for phase-based performance assessment. The procedure consists of three normalization steps: currency conversion, location adjustment, and time adjustment considering various scenarios where the required information is not fully identified. This paper also presents how the cost normalization procedure has been applied to the 10-10 Performance Assessment Program, which is a phase-based performance assessment system developed by the Construction Industry Institute (CII). Both researchers and industrial professionals can apply the cost normalization procedure to studies and practices regarding to cost estimation, feasibility analysis, and performance assessment.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.590-598
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• 2022

Benchmarking is an important tool to assess the performance of capital projects in the construction industry. Incorporating cost-related metrics into a benchmarking system requires an effective cost normalization process to enable meaningful comparisons among projects that were executed at different locations and times. Projects in the downstream and chemicals sector have unique characteristics compared to other types of construction projects, they require a distinctive cost normalization framework to be developed to benchmark their absolute cost performance. The purpose of this study is to develop such a framework to be used for the case of benchmarking the downstream and chemical projects for their performance assessment. The research team started with a review of existing cost normalization methodologies adopted in benchmarking systems and conducted 7 interviews to identify the current cost normalization practices used by industrial professionals. A panel of 12 experts was then convened and it held 6 review sessions to accomplish the framework development. The cost normalization framework for benchmarking downstream and chemical projects was established as a three-step procedure and it adopts a 4-element cost breakdown structure to accommodate projects submitted by both owners and contractors. It also incorporated 5 published cost indexes that are compatible with downstream and chemical projects and they were embedded into 2 options to complete the normalization process. The framework was then pilot-tested on 4 completed projects to validate its functional practicality and the downstream and chemical use case in the benchmarking system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.801-809
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• 2023

Road construction projects have a long duration so cost overruns and schedule delays are occurred. However, performance assessment system that can manage and prepare for this in advance is insufficient. In addition, road construction are affected by many factors during under construction. Therefore it is necessary to conduct performance assessment considering the characteristics of roads and prepare for similar projects in the future. The purpose of this study is to provide a framework to evaluate construction phase performance and present a performance management plan using road construction information. Also, This study conducted time adjustment between the start and the finish of the project and developed performance metrics based on absolute and relative indicator. This study analyzed the cost, schedule, and changes of the road project construction process, showing the possibility of advancement of performance assessment and how to use it when planning new road construction projects.

• Structural Monitoring and Maintenance
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• v.6 no.3
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• pp.219-235
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• 2019

Scheduled inspections of common crossings are one of the main cost drivers of railway maintenance. Prognostics and health management (PHM) approach and modern monitoring means offer many possibilities in the optimization of inspections and maintenance. The present paper deals with data driven prognosis of the common crossing remaining useful life (RUL) that is based on an inertial monitoring system. The problem of scheduled inspections system for common crossings is outlined and analysed. The proposed analysis of inertial signals with the maximal overlap discrete wavelet packet transform (MODWPT) and Shannon entropy (SE) estimates enable to extract the spectral features. The relevant features for the acceleration components are selected with application of Lasso (Least absolute shrinkage and selection operator) regularization. The features are fused with time domain information about the longitudinal position of wheels impact and train velocities by multivariate regression. The fused structural health (SH) indicator has a significant correlation to the lifetime of crossing. The RUL prognosis is performed on the linear degradation stochastic model with recursive Bayesian update. Prognosis testing metrics show the promising results for common crossing inspection scheduling improvement.

• Korean Journal of Construction Engineering and Management
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• v.24 no.1
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• pp.3-11
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• 2023

According to the post evaluation scheme in Korea of a public construction project which is more than 30 Billion KRW, project performance is evaluated by investigating outcomes and effects of the construction after the completion of the project. The current post evaluation results can be used for planning and estimating a construction project in the future. However, it is not easy to utilized for an on-going project because the system does not provide the phase-based performance of a project. Although project planning performance is important for project initiation, few attempt has been made to evaluate planning performance in Korea. The purpose of this study is to provide a conceptual performance evaluation of planning performance using preliminary feasibility study conducted by Korea Development Institute. This study developed a planning performance database using data extracted from preliminary feasibility study reports of the completed 354 road construction projects. This study analyzed the performance of the planning stage of road projects by developing absolute metrics such as standard construction cost and standard construction schedule based on a Lane-Km. Using the standard construction cost and schedule metrics, the planning performance was analyzed by project characteristics. The results of this study can be used for phase-based performance evaluation from planning phase to construction phase.

• Steel and Composite Structures
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• v.52 no.2
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• pp.145-163
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• 2024

This paper presents six novel hybrid machine learning (ML) models that combine support vector machines (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with the Harris Hawks Optimization (HHO) algorithm. These models, namely HHO-SVM, HHO-DT, HHO-RF, HHO-GB, HHO-XGB, and HHO-CGB, are designed to predict the ultimate strength of both rectangular and circular reinforced concrete (RC) columns. The prediction models are established using a comprehensive database consisting of 325 experimental data for rectangular columns and 172 experimental data for circular columns. The ML model hyperparameters are optimized through a combination of cross-validation technique and the HHO. The performance of the hybrid ML models is evaluated and compared using various metrics, ultimately identifying the HHO-CGB model as the top-performing model for predicting the ultimate shear strength of both rectangular and circular RC columns. The mean R-value and mean a20-index are relatively high, reaching 0.991 and 0.959, respectively, while the mean absolute error and root mean square error are low (10.302 kN and 27.954 kN, respectively). Another comparison is conducted with four existing formulas to further validate the efficiency of the proposed HHO-CGB model. The Shapely Additive Explanations method is applied to analyze the contribution of each variable to the output within the HHO-CGB model, providing insights into the local and global influence of variables. The analysis reveals that the depth of the column, length of the column, and axial loading exert the most significant influence on the ultimate shear strength of RC columns. A user-friendly graphical interface tool is then developed based on the HHO-CGB to facilitate practical and cost-effective usage.