Shu-Chien Hsu

Understanding the relationship between safety investment and safety performance of construction projects through agent-based modeling

The construction industry in Hong Kong increased its safety investment by 300% in the past two decades; however, its accident rate has plateaued to around 50% for one decade. Against this backdrop, researchers have found inconclusive results on the causal relationship between safety investment and safety performance. Using agent-based modeling, this study takes an unconventional bottom-up approach to study safety performance on a construction site as an outcome of a complex system defined by interactions among a worksite, individual construction workers, and different safety investments. Instead of focusing on finding the absolute relationship between safety investment and safety performance, this study contributes to providing a practical framework to investigate how different safety investments interacting with different parameters such as human and environmental factors could affect safety performance. As a result, we could identify cost-effective safety investments under different construction scenarios for delivering optimal safety performance.

Knowledge Management for Risk Hedging by Construction Material Suppliers

AbstractThe cost of construction materials has significantly increased in recent years, and construction material suppliers have started to utilize investment derivatives to mitigate risks. While much knowledge has been established on the predictions of using derivatives for risk-hedging, little is known about the evaluation of the risk mitigation by analyzing financial status of construction material suppliers. This paper presents a knowledge-sharing model to determine whether risk mitigation based on the use of derivatives would be beneficial to the companies. This model is developed by first establishing a comprehensive database comprising 560 financial reports on business capacity of construction material suppliers, followed by combining the technique for order preference by similarity to ideal solution (TOPSIS) and k-nearest neighbor (KNN) pattern classification. The benefits of the research described include a knowledge-sharing mechanism in regard to the behaviors of related construction material su...

Determining and Classifying Factors of Employees’ Expatriation Willingness Using Rough Set Theory

AbstractThe objective of this research is to identify and classify the factors affecting the expatriation willingness (EW) of engineering consulting company employees. A total of 13 EW impact factors are summarized from a review of the literature and divided into four categories. From the collected factors and expert interviews, 22 impact factors are obtained and divided into eight categories, with the exception of demographic variables. A survey aiming at the top five engineering consulting companies is carried out. Out of a total of 1,000 questionnaires sent out, 41.3% valid responses are returned. The statistical analysis shows that the survey is reliable and one of the 22 factors is removed. Rough set theory (RST) is utilized to classify these factors into three classes based on impact level. The conclusions provide practitioners with six core impact factors on employees’ EW. The findings can be of benefit to employers, helping them to save recourse and to target the most appropriate employees for exp...

Spatial Agent-Based Model for Environmental Assessment of Passenger Transportation

AbstractThis study presents an urban transportation simulation model for lifecycle environmental performance evaluation (ALENT). ALENT integrates geographic information to quantify stations’ access...

Forecasting spatial dynamics of the housing market using Support Vector Machine

This paper adopts a novel approach of Support Vector Machine (SVM) to forecast residential housing prices. as one type of machine learning algorithm, the proposed SVM encompasses a larger set of variables that are recognized as price-influencing and meanwhile enables recognizing the geographical pattern of housing price dynamics. The analytical framework consists of two steps. The first step is to identify the supporting vectors (SVs) to price variances using the stepwise multi-regression approach; and then it is to forecast the housing price variances by employing the SVs identified by the first step as well as other variables postulated by the hedonic price theory, where the housing prices in Taipei City are empirically examined to verify the designed framework. Results computed by nonparametric estimation confirm that the prediction power of using SVM in housing price forecasting is of high accuracy. Further studies are suggested to extract the geographical weights using kernel density estimates to reflect price responses to local quantiles of hedonic attributes.

A Decision Model for Technology Selection in Renovation Project Planning

As infrastructure ages, a significant portion of US capital expenditure is incurred for the renovation and retrofit of existing facilities. Renovation projects are complex, high risk, and involve extensive coordination and planning, primarily due to uncertain site conditions and continuing operation requirements of existing facilities. Early studies have identified innovative and emerging technologies that facilitate site investigation and align renovation and maintenance activities with business production and operation schedules. However, the selection of appropriate technologies in specific project and business environments remains a challenge. Particularly, there is no guidance for project teams who must incorporate the selected new technologies into scope management and project planning. This paper establishes a framework to guide project teams towards appropriate technology selection in renovation project planning. A decision model is developed to integrate the technology selection process into a widely used scope management tool, Project Definition Rating Index (PDRI). The model is specially designed and flexible enough to allow project teams to decide appropriate technologies according to various criteria, including cost, application area, and risk mitigation.

Board of Director’s Role in Preventing Corporate Misconduct in the Construction Industry

This paper examines the theoretical literature and conducts empirical analysis on the mechanisms of corporate governance. We focus on directors’ role in ameliorating various types of agency problems (e.g., corporate misconduct) arising from conflicts of interests between stakeholders and managers to the firm. In a sample of 45 public listed construction companies in Taiwan between 2005 and 2014, the result indicates that illegal corporate behavior is more likely to occur in firms with large boards while board independence isn’t significant for misconduct constraining. The finding also shows why board’s monitoring function is important and how firms rein in misconduct by altering board size and composition.

A Budget Allocation Model for Flood Managementin Flood-Prone Areas

Global climate changesfrequently cause severe floodingwhich damage peoples propertiesand livesin Taiwan. As a result, the Taiwan government initiated a long-term plan to prevent flooding by regulating 130 rivers in flood-prone areas. However, the budget is limited and cannot meet the total cost of the hydraulic engineering of the 130 rivers. How to objectively and efficiently prioritize rivers that need to be regulated remains a major challenge for the government. This research collected the information of regulating 130 rivers in terms of their costs and benefits; then it utilized a hybrid method that integratesboththedynamic programming and the Paretofront analysis (designated as DPPF here) to develop a budget allocation model.The results show that this model can help the government to save up to NT

Hybrid ANN-CBR model for disputed change orders in construction projects

2.153 billionannually.