The body of knowledge in decision making about international construction market entry has been primarily focused on identifying risk factors, quantifying risks, and developing risk management strategies for construction firms to be more likely to succeed in international markets (e.g., risk registrars, industry checklists, entry procedures, and quantitative/qualitative risk assessment models). Going international can also be considered as a large-scale investment for a firm because it has all three major characteristics of an investment decision: (1) entering an international market requires a substantial level of initial outlays; (2) this initial capital is not completely recoverable if the firm decides to exit the market; and (3) the success of an international construction business venture is subject to great market uncertainty, which is exogenous to the firm. Traditionally, financial valuation of international construction markets is conducted by net present value (NPV) analysis approach. Alt...

International construction projects normally involve high levels of risk. To reduce such risk, contractors typically include a reserve to mitigate cost overruns and schedule delays. This reserve comprises two categories: (1) a contingency reserve covering potentially required changes, and (2) a management reserve (MR) covering unplanned changes. Since estimating an MR is difficult because the inherent risks are unpredictable, the traditional percentage method is still used for this purpose. To address this shortcoming, this study proposes an MR estimation method based on the cost and schedule performance ratios of international construction projects. K-nearest neighbor (k-NN) is applied to retrieve similar projects and a genetic algorithm is adopted to optimize the retrieved cases. Finally, case studies are conducted to validate the estimated ratios and test the applicability of the proposed MR estimation method. The case study results show that the proposed method can infer the cost and schedule performance ratios within a 5% error rate. As a result, it can estimate the MR more accurately than the traditional percentage method.

The construction cost index (CCI) is a quantitative construction-cost indicator proposed by Engineering News–Record (ENR). Because predicting CCI is crucial to the investment planning, bidding, and profitability of construction projects, considerable effort has been devoted to CCI estimation, and substantially accurate results have been obtained. However, these findings were based on the assumption of a Gaussian distribution of data, which limits the estimation accuracy for fluctuating data. To overcome the limitation of such conventional methods, the present study aimed to refine CCI prediction performance by applying the concept of long memory. First, the existence of long memory in CCI is examined by performing rescaled range (range/scale or R/S) analysis. Second, a time-series model was developed: the autoregressive fractionally integrated moving average (ARFIMA) model, which reflects the characteristics of long memory. Finally, the prediction performance of the ARFIMA model was compared with that of the conventional autoregressive integrated moving average (ARIMA) model. CCI data from January 1990 to August 2016 were used to develop these models. The results showed that ARFIMA outperformed ARIMA in terms of prediction performance, on average, by 9.5%. The ARFIMA model achieved higher CCI prediction performance by incorporating the properties of long memory. In summary, the results confirmed the importance of applying long memory to CCI predictions. Furthermore, the developed model could play a key role in improving the accuracy of cost estimation in the construction market.

While there have been many studies on life cycle cost analysis and preventive maintenance planning, this study proposes an innovative method of bridge asset management in South Korea. Two different levels of approaches were used in this study. First, in the level of bridge elements, deterioration modeling and optimized maintenance repair and rehabilitation (MR&R) planning on bridge assets are proposed, using the bridge historical data of Han River in the city of Seoul. Second, the network level of bridge asset management is suggested, using historical MR&R cost and budget, overall-condition assessment results, and health index data. These two levels of approaches were developed into an Internet-based application so that facility managers can use them to review their past budgets and to plan their future budget based on historical data.

Flooding has the ability to severely reduce the capacity of a transportation network. The closure of even a single bridge, which often acts as a critical link in transportation networks, can have a severe impact on the entire network. This impact can lead to significant economic costs resulting from increased travel distances for drivers. Despite the significance of these costs, however, notably few studies have been conducted to determine the societal economic cost that would be incurred due to bridge closures. One possible reason for the lack of studies investigating bridge closures due to flooding could stem from the difficultly in collecting data. To address this issue, the methodology presented in this paper uses modeling and data resources that are available for major cities in most developed countries, including those in South Korea. We evaluate the economic impact of the bridge closures using the new administrative capital of North Gyeongsang Province, South Korea as a case study. Scenarios for the closure of bridges are derived from channel surveys and hydraulic analyses. These methods are used to overcome a lack of adequate data on historical floods in the new city. Traffic is forecasted to estimate the number of road users that would be forced to take detours due to inundated bridges. Contrasting travel distances when bridges are and are not operational, economic costs incurred by bridge closures due to flooding are estimated. The results indicated that bridge closures would result in an economic cost of 1563 USD to 44,180 USD per day, depending on how many bridges are closed and how many people are living in the new city. The estimates from this study will act as guidelines for identifying cost-effective mitigation and preparedness strategies aimed at reducing the frequency and impact of bridge closures due to flooding.

Internal communication is pivotal to a company’s performance during construction projects. Although internal communication is known to be strongly affected by the type of business, it is still unclear which influence factors have a greater impact. This research aimed to investigate the causal relationship between internal communication effectiveness and company performance for the different business types within the construction industry, namely owners, contractors, and architects/engineers. Structural equation modeling was applied to analyze the influence factors of 52 measurement variables and 11 latent variables. Based on the results of 317 completed surveys collected from 15 construction-related firms, the authors successfully investigated crucial factors in company performance according to the different business types. The results showed that vertical information flows are more influential in organizational performance than horizontal information flows due to hierarchical organizational characteristics common to most construction-related firms. The results assist managers in evaluating their internal communication based on the company’s business type and developing organizational strategies to mitigate any communication-related issues. This research contributes to the construction industry’s body of knowledge by demonstrating the influence factors crucial to internal communication effectiveness for different business types.