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A quantitative risk assessment development using risk indicators for predicting economic damages in construction sites of South Korea A quantitative risk assessment development using risk indicators for predicting economic damages...
Kim, Ji-Myong; Kim, Taehui; Son, Kiyoung; Bae, Junseo; Son, Seunghyun
2019-09-03 00:00:00
JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2019, VOL. 18, NO. 5, 472–478 https://doi.org/10.1080/13467581.2019.1681274 CONSTRUCTION MANAGEMENT A quantitative risk assessment development using risk indicators for predicting economic damages in construction sites of South Korea a a b c d Ji-Myong Kim , Taehui Kim , Kiyoung Son , Junseo Bae and Seunghyun Son a b Department of Architectural Engineering, Mokpo National University, Muan, Republic of Korea; School of Architectural Engineering, University of Ulsan, Ulsan, Republic of Korea; Engineering and Physical Sciences, University of the West of Scotland, Paisley, UK; Department of Architectural Engineering, Kyung Hee University, Seoul, Republic of Korea ABSTRACT ARTICLE HISTORY Received 6 January 2019 The purpose of this study is to suggest a quantitative risk assessment approach for construction Accepted 30 September 2019 sites using risk indicators to predict economic damages. The frequency of damage in building construction has recently increased, and the associated costs have been increased as well. KEYWORDS Although a request for a damage estimation model has been extended, the industry still lacks Risk assessment; quantitative and comprehensive research that reveals the physical relationship between construction project; damage and risk indicators. To address that issue, we use an insurance company’s payouts insurance; natural hazards; from construction site claims in South Korea to reflect the real financial damage. We adopted regression analysis a multiple regression method to define the risk indicators: geographic vulnerability, natural hazards, capability, and general project information. The results and findings of this research will be accepted as an essential guideline for developing a construction risk estimation model. 1. Introduction measures against disaster risk are likely to cost more than the damages caused by the accidents that do The obvious recent trends in construction projects are occur (Emmett and Therese 1995). Therefore, man- a larger size and increasing process complexity. For agers need risk assessment models to numerically ana- example, the Korean Statistical Information Service lyze their risks. reports that of the total construction contracts from In this study, we analyze the financial loss records 2006 to 2015, 92.8% were large projects, with a total from actual construction sites in South Korea and sta- cost of more than 10 billion KRW (Korea statistical tistically examine the relationships between the yearbook ([2005] 2014)). damages and risk indicators. We use the insured As construction project sizes increase, the risk and claim payouts of an insurance company to reflect the uncertainty associated with construction projects rises real financial damages caused by accidents. We then as well. Insurance Statistics Information Services (INSIS) develop the risk indicators and risk assessment model reports that the total amount of loss in the construc- using a multiple regression analysis. tion industry increased 37.6%, from 54.7 billion KRW in The results of this research will be acknowledged as 2006 to 87.7 billion KRW (Insurance statistical month- an essential guideline for developing a construction book ([2006] 2015). risk estimation model in South Korea. Moreover, Moreover, the Korea Occupational Safety and Health other developing countries similar to South Korea Agency notes that although the accident rate across all with respect to the construction environment and nat- industries declined by 29.7%, from 0.84% in 2004 to ural disasters can be adopted the framework and find- 0.59% in 2013, with a frequency rate for injury of ing of research. We also expect our results to develop 2.95% in 2013, the accident and frequency rates in the in the future through continuous feedback on the construction industry were 0.92% and 4.96%, respec- accumulated effects and data verification. tively, in 2013, 1.6 and 1.7 times higher than the overall rate MOEL (2015). Thus, the construction industry has a higher risk than the overall industrial sector. 2. Methodologies of construction risk However, the nature of construction, e.g., the long- assessment evity of the projects and the many variations of field- Even though much research has suggested risk indica- work, is an obstacle to accurate risk analysis and tors and risk assessment models for construction, management because of its fluctuations and uncer- quantitative and comprehensive studies are still tainties Kim, Kang, and Park (2010). Precautionary required to estimate the financial risks of construction CONTACT Seunghyun Son sshway6692@naver.com Department of Architectural Engineering, Kyung Hee University, 1732 Deoyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, Republic of Korea This article has been republished with minor changes. These changes do not impact the academic content of the article. © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the Architectural Institute of Japan, Architectural Institute of Korea and Architectural Society of China. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 473 projects. (Kuo and Lu 2013) investigated a risk- to classify the significant risk indicators and describe assessment approach in accordance with numerous the relationship between the damage and each risk previous studies and interviews. They defined several indicator. risk factors, such as natural hazards and the ability of the constructor (Kuo and Lu 2013). Chan et al. (2011) 3.1. Dependent variable defined risk indicators and ranked them for construc- tion projects using a questionnaire survey of various We used insurance claims from the construction site categories of examinees. from 2000 to 2016. The total number of data was 430. They recommended several risk factors, such as nat- The dependent variable is a percentage of damage, the ural hazards, ability of the builder, and site environment damage ratio. Each damage ratio is estimated as the Chan et al. (2011). Hsueh et al. (2007) tested a risk amount of damage (KRW) divided by the total cost of assessment practice on the cases studied and suggested the construction project (KRW). a risk assessment model for construction projects using factors such as specific project information and inside 3.2. Independent variable and outside factors Hsueh et al. (2007). (Akintoye and MacLeod 1997) proposed risk management and a risk We use four categories of risk indicators in accordance analysis of methodologies based on a questionnaire with previous studies: geographical risk, natural survey of field managers and construction contractors. hazards, construction ability, and project information, They used several risk indicators, including natural as shown in Table 1. hazards, construction companies, and project informa- Independent variables used in this study are essen- tion (Akintoye and MacLeod 1997). (Choi and tial data that must be input when joining an insurance Mahadevan 2008)offered a risk assessment approach company or indispensable for the evaluation of for construction projects using a databank. a subscription. Therefore, there was no missing infor- They chose critical factors to estimate the amount of mation in the data used in this sample. risk in a construction project, such as natural disasters and construction-related risk factors (Choi and Mahadevan 3.2.1. Geographical risk indicators 2008). Bing et al. (2005) used a questionnaire survey to Even though, much research has suggested risk indica- identify risk indicators (constructor ability, site conditions, tors and risk assessment models for construction, quan- and so on) and suggested risk distribution methods in titative and comprehensive studies are still required to construction projects Bing et al. (2005). estimate the financial risk in a construction project. Kuo In conclusion, even when researchers have identi- and Lu (2013) conduct a study to investigate the risk fied risk indicators and proposed methods for assessment approach in accordance with the numerous approaching risk assessment models, they did not previous studies and interviews. He defines several risk test the relationship between the risk indicators and factors such as natural hazards, and ability of construc- actual damage to determine whether the relationships tor, and so on (Kuo and Lu 2013). are significant. Filling that gap requires a statistical Chan et al. (2010) define the risk indicators and its investigation. ranking for construction project basic of questionnaire Moreover, it might be possible that risk indicators survey from various categories of examinees. He from other places are not significant for construction recommends several risk factors such as natural sites in other places. The difference in natural and hazards, the ability of builder, site environment, etc. construction environments among countries might (Chan et al. 2011). change the sensitivity of the indicators. Hence, we Hsueh et al. (2007) research a risk assessment prac- adopt the various risk indicators from previous studies tice is found on the cases studied and suggests a risk and statistically examine them against damage in this assessment model for construction projects utilizing comprehensive study. several risk factors such as specific project information, inside and outside factors of a construction project (Hsueh et al. 2007). 3. Research methodology and variables Akintoye and MacLeod (1997) propose risk manage- We process four major phases to determine the risk ment and risk analysis of methodologies based on indicators and identify the correlation between the risk a questionnaire survey from the field managers and con- indicators and damage. struction contractors. He used several risk indicators such First, we select the key risk indicators based on natural hazards, construction company, and project infor- previous studies. Second, we collect the dependent mation (Akintoye and MacLeod 1997). Choi and variable, i.e., the claim payouts from construction Mahadevan (2008)offer a risk assessment approach for sites, from an insurance company. Third, we gather construction project utilizing a databank. He choose cri- each independent variable from the relevant construc- tical risk factors to estimate the amount of risk in tion sites. Fourth, we use a multiple regression method a construction project such as natural disasters, 474 J.-M. KIM ET AL. Table 1. Risk indicators. Category Risk indicator Explanation Component Geographical vulnerability Elevation Elevation above sea level (m) Numerical Field location Location of the construction site 1.Suburban 2.Urban 3.Metropolitan Natural Tropical cyclone Tropical cyclone risk 0. Area 0:74–141 km/h hazards (expected wind speed) at the field 1. Area 1:142–184 km/h 2. Area 2:185–212 km/h 3. Area 3:213–251 km/h 4. Area 4:252–299 km/h 5. Area 5:More than 300 km/h Flood Flash flood risk 1. Area 1:1 time per a year (expected yearly incidence of flooding) 2. Area 2:2 times per a year at the field 3. Area 3:3 times per a year 4. Area 4:4 times per a year 5. Area 5: 5times per a year 6. Area 6:6 times per a year Construction ability ENR Engineering News Numerical Record ranking Project Structure type Type of building 1. Other information (Dummy variable) 2. Reinforced concrete Total months Total length of construction period (months) Numerical Progress rate Progress rate at the time the incident occurred Percentage Floor Number of floors Numerical Under ground Number of underground levels Numerical construction interrelated risk factors, and so on (Choi and flooding: zone 0 is the area with an expected earth- Mahadevan 2008). Bing et al. (2004) identify the risk indi- quake intensity of MM (Modified Mercalli scale)Ⅴ and cators, e.g., constructor ability, site conditions, and so on, lower, zone 1 is the area with an expected earthquake suggest the risk distribution methods in construction intensity of MMⅥ, zone 2 is the area with an expected projects using questionnaire survey (Bing et al. 2005) earthquake intensity of MM Ⅶ, zone 3 is the area with In conclusion, even if the studies have identified the an expected earthquake intensity of MM Ⅷ, and zone risk indicator and propose methods for approaching risk 4 is the area with an expected earthquake intensity assessment models, there is a gap that the indicators of MM Ⅸ and higher. would be need an statistical investigation to test the The tropical cyclone risk is grouped into 6 sections relationship with risks indicators and actual damage in terms of the expected wind speed: zone 0 is the area whether the relationships are significant or not. with an expected wind speed of 76 to 141 km/h, zone 1 Moreover, it might be possible that the risk indica- is the area with an expected wind speed of 142 to tors would not be significant for construction sites in 184 km/h, zone 2 is the area with an expected wind other countries. The difference in natural and construc- speed of 185 to 212 km/h, zone 3 is the area with an tion environment among the countries might change expected wind speed of 213 to 251 km/h, zone 4 is the the sensitivity of indicators as well. Hence, this study area with an expected wind speed of 252 to 299 km/h, adopts the various risk indicators found from the pre- and zone 5 is the area with an expected wind speed of vious studies and statistically exam the indicators with more than 300 km/h. damage as a comprehensive study. 3.2.3. Construction ability indicators 3.2.2. Natural hazards indicators Many studies have found construction ability to be a key Natural hazards play a main role in defining the risks in indicator of construction risk (Carr and Tah 2001;Chan construction (Choi and Mahadevan 2008; Kuo and Lu et al. 2011; Choi and Mahadevan 2008;Kuo andLu 2013). 2013). Weather-related natural disasters directly cause The productivity and quality of construction work and construction delays, so floods, tropical cyclones, heavy safety in construction sites are significantly related to the snow, etc., are clear construction risks (Akintoye and design, engineering, and skill levels of management MacLeod 1997; Carr and Tah 2001; Chan et al. 2011; El- (Akintoye and MacLeod 1997). Sayegh 2008). For example, a team experienced in engineering To represent the risk of natural hazards, we use the and design can conduct a well-managed procurement Natural Hazards Assessment Network World Map of plan and respond quickly to sudden changes in design. Natural Hazards from the Munich Reinsurance An experienced management team can elicit increased Company to assess the risk of floods and tropical productivity from site workers and ultimately reduce cyclones. The worldwide natural hazard map repre- the project price and period. In addition, highly experi- sents the risk by grades. For example, the flood risk is enced managers on a construction site can prevent scaled into five parts based on the occurrence of poor construction work. They can thus ensure a high- JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 475 quality product and prevent reworking to correct Table 2. Descriptive statistics. Variables Min. Max. Mean Std. Deviation faults. Furthermore, polished construction plans Dependent drawn by a highly skilled contractor can promote Damage ratio 0.34 593.91 44.77 91.40 good communication and control between subcon- Independent tractors and the customer, thereby preventing unex- Elevation 0.00 792.00 45.96 51.53 Field location 1.00 3.00 2.42 0.71 pected delays and the occurrence of excess costs (Kuo Tropical cyclone 0.00 4.00 1.42 0.87 and Lu 2013). Flood 3.00 6.00 4.85 0.51 ENR 1.00 100.00 41.82 42.77 In this research, we adopt the Engineering News Structure type 1.00 2.00 1.66 0.48 Record (ENR) ranking to reflect construction ability. Total month 5.00 61.00 23.85 12.27 ENR is a weekly magazine that covers various areas of Progress rate 0.00 0.99 0.42 0.30 Floor 1.00 56.00 17.57 10.00 the construction industry and delivers information, Underground 0.00 7.00 3.33 2.12 analysis, etc. ENR also provides an annual ranking based on its yearly survey of gross revenue for inter- national and US companies. Table 3. Summary of the model. Sum of Mean Adj- 2 2 Model Squares Square F Sig. R R 3.2.4. Project information indicators Regression 487.501 81.25 48.278 0.000 0.465 0.457 The key features of a construction project can describe Residual 686.652 1.683 Total 1174.153 the amount of risk, which is computable based on the difficulty, complexity, and size of the construction project NDOT(2012). The structure type delineates the difficulty Table 4. Coefficients of the model. of construction. For instance, reinforced concrete con- Indicators Β Std. Error Beta Sig. VIF struction faces more hazards and accidental events than Constant 3.099 0.389 0.000 other construction types (Gurcanli, Bilir, and Sevim 2015). Geographical vulnerability Moreover, a project’s complexity can predict the Field location 0.286 0.096 0.122 0.003 1.174 progress rate. NDOT reports a relationship between Natural hazards the progress rate and risk amount because the amor- Tropical cyclone 0.159 0.077 0.082 0.039 1.101 Project information tizable and acknowledged risk increases as the project Structure type 0.510 0.157 0.144 0.001 1.385 progresses (NDOT 2012). In addition, a project’s size Total month −0.074 0.005 −0.550 0.000 1.071 can also reveal the amount of risk. Floor −0.016 0.007 −0.094 0.028 1.280 Underground −0.114 0.039 −0.144 0.004 1.691 For example, Kim et al. (2015) statistically investigated the relationship between the financial loss caused by a natural disaster and the vulnerability of the built envir- tropical cyclone, structure type, total month, floor, and onment. They found a significant connection between underground, are identified against the damage ratio. the size of a property and loss, such that a larger property We exclude the other indicators because the is less vulnerable than a smaller property (Kim et al. 2015; P-values are larger than 0.10. The weights of the var- Chan et al. 2011;Kuo andLu 2013). In this study, we use iance inflation factor (VIF) are from 1.071 to 1.691, the following variables: the structure type is a dummy which indicates that none of the significant indicators variable, reinforced concrete and others; the total month have severe multicollinearity. indicates the total time for the construction project in months; the progress rate is the percentage complete at the time of the incident; and the number of floors and 4.1. Inquiry of the model underground levels reflect the difficulty, complexity, and The residual plot investigates homoscedasticity Figure size of the construction project. 1. The disorderly spread shapes of the residuals prove that they are randomly distributed. This verifies that 4. Data analysis the residuals’ variance is continuous. Moreover, the histogram of the residuals and the Q–Q Table 2 shows the descriptive statistics for the independent plot confirm that the model residuals are normally spread variables and the dependent variable. We use the backward (Figure 2). Furthermore, we use the Kolmogorov-Smirnov elimination method to obtain the ideal-fit regression model. value to check the normality of the residuals and find that Table 3 presents the summary of the regression model. the residuals are ordinarily dispersed: the P-value of 0.200 We transform the damage ratios by the natural log. is larger than 0.05, as seen in Table 5. The P-value of 0.000 is less than 0.05, which reveals that the model is statistically significant. The adjusted R-square value of 0.457 signifies that this relationship 4.2. Model validation describes 45.7% of the variance. The coefficients of the regression model are listed in Figure 3. represents a scatter plot of the predicted log- Table 4.Six significant indicators, including field location, transformed damage ratio and the actual log- 476 J.-M. KIM ET AL. Table 5. Test of model normality. Kolmogorov-Smirnov Statistic Sig. Ln(Damage ratio) 0.028 0.200 Figure 1. Residuals plot for the model. Figure 3. Plot of predicted vs. actual damage ratio. that the remainder of the variability is explainable using currently unidentified variables. 5. Discussion In this research, we found a statistically significant model that can elucidate 45.7% of the variability in the relationship between the damage and the risk indicators. The significant indicators are field location, (a) Histogram of residuals the risk level of tropical cyclone, structure type, total months of the construction period, number of total floors, and number of total underground levels. Among the geographical vulnerability indicators, the field location has a positive association with damage, which denotes that a construction site in a large and crowded city is exposed to more risks than a construction site in a rural area. This outcome confirms the found the field location to be a significant indicator of damage and a valuable factor in estimat- ing the damage at construction sites (Chan et al. 2011; Kuo and Lu 2013). Among the natural hazard indicators, the risk level for a tropical cyclone is significantly related with damage. Thus, if the risk level increases, the damage and the importance of loss-prevention activities also increase. This result also supports studies (Akintoye (b) Q-Q plot and MacLeod 1997; Carr and Tah 2001; Chan et al. 2011; El-Sayegh 2008). Figure 2. Histogram of residuals and Q–Q plot. Among the project information factors, the struc- ture type, which is a dummy variable, is statistically transformed damage ratio. The adjusted R-square of significant, and the positive relationship indicates that the model is 0.457, which indicates that the significant reinforced concrete construction sustains more indicators can explain 45.7% of the variability in the damage than other construction types. This result sup- damage ratio, the dependent variable. It also indicates ports the finding of a previous study that found that JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 477 reinforced concrete construction has more latent In the future, it is required that the establishment of exposure to hazards and accidents than other con- the database for reliable risk indicators and construc- struction types (Gurcanli, Bilir, and Sevim 2015). The tion risk prediction models through continuous feed- other indicators, i.e., total months of the construction back of accumulated effects and data verification and period, number of total floors, and number of total development of a computerized system. underground levels, all have negative relationships with damage, which indicates that the size of 6. Conclusion a construction project affects the risk of damage. This result corroborates former research (Kim et al. 2015). The frequency of damage at construction sites has The Table 6 represent that the summary of compar- recently increased, and the amount of damage has ison results with previous studies. The results of this grown as well. Even though the demand for study were similar to those of previous studies, and the a damage estimation model has increased, quantita- sign was the same. tive research that discloses the relationship between This study used an insurance company’s payouts damage and risk indicators has remained limited. from construction site claims in South Korea to identify To fill that gap, we use an insurance company’s risk indicators and the correlation between risk indica- claim payouts from construction sites in South Korea tors and economic damages was analyzed. to identify risk indicators and test their correlations In this respect, this study provides a quantitative risk with actual financial damage as an initial stage toward assessment method that predicts economic damage. developing a construction damage assessment model. As a result, this study can provide practically the impor- In our statistical analysis, we found six significant indi- tant criteria for proper decision-making in order to cators that explain the damage ratio: field location, manage and reduce losses in the assessment of eco- tropical cyclone risk, structure type, total months, num- nomic risks of the construction site. ber of floors, and number of underground levels. Those For example, as shown in Figure 4, when project results will offer construction companies, project own- owners evaluate the risk of a construction site, risk ers, and insurance companies a critical standard to indicators can be used to predict economic damage manage and diminish losses when they assess the to a construction site. If these economic damages are financial risks at construction sites. For example, com- within acceptable limits, then the project will proceed. panies that need to measure the potential economic If not, alternatives that can reduce or minimize eco- loss risk in a particular building or group of buildings, nomic damages should be established. such as insurance and reinsurance companies, could use this model. To measure potential risks, they will be able to reconstruct or create in-house models using the methodology used in this paper. They can certainly Table 6. Comparison of results with previous studies. assess risk, make quick decisions, and use the resulting Indicators Previous Studies Significant Sign loss ratio as the default loss rate. They can also use the Field location Chan et al. (2011), Kuo and Lu ○ + model to test and adjust the vendor models. It can be (2013) used as an imperative material to compare and judge Tropical cyclone Akintoye and MacLeod (1997), ○ + Carr and Tah (2001), Chan et al. the results based on their portfolio, business prefer- (2011), El-Sayegh (2008) ence, and risk appetite. Structure type Gurcanli, Bilir, and Sevim (2015) ○ + Total month Kim et al. (2015), Chan et al. ○ - Furthermore, utilizing the risk indicators and estab- (2011), Kuo and Lu (2013) lished the method in this research would be able to Floor Kim et al. (2015), Chan et al. ○ - apply other countries which have a similar geographi- (2011), Kuo and Lu (2013) Underground Kim et al. (2015), Chan et al. ○ - cal vulnerability, natural hazards, and architectural (2011), Kuo and Lu (2013) environment with South Korea. Nevertheless, the data set used in this research is the damage data of one insurer in South Korea. Using different data from different countries may have different consequences due to the nature of the construction project being affected by various external factors. Hence, in order to support the results of this study, further studies are needed through the extraction of data from different countries and other data sets. Furthermore, the adjusted R value was 0.457 which means that the residual variability of the damage is clarified by some hidden indicators. Future research will need to define other potential indicators and add to the model to enhance the explanatory power of the model. Figure 4. Economic risk assessment method. 478 J.-M. KIM ET AL. Disclosure statement International Journal of Project Management 29 (6): 751–763. doi:10.1016/j.ijproman.2010.08.003. No potential conflict of interest was reported by the authors. Choi, -H.-H., and S. Mahadevan. 2008. “Construction Project Risk Assessment Using Existing Database and Project-specific Information.” Journal of Construction Funding Engineering and Management 134 (11): 894–903. doi:10.1061/(ASCE)0733-9364(2008)134:11(894). This work was supported by the National Research Cutter, S. L., B. J. Boruff, and W. L. Shirley. 2003. “Social Foundation of Korea (NRF) grant funded by the Korea gov- Vulnerability to Environmental Hazards.” Social Science ernment (MOE) (NRF-2019R1F1A1058800). Quarterly 84 (2): 242–261. doi:10.1111/ssqu.2003.84.issue- El-Sayegh, S. M. 2008. “Risk Assessment and Allocation in the Notes on contributors UAE Construction Industry.” International Journal of Project Ji-Myong Kim got the Ph.D from Texas A&M University then Management 26 (4): 431–438. doi:10.1016/j. he is working as a professor in Mokpo National University, ijproman.2007.07.004. Republic of Korea. His major area is construction Emmett, J. V., and M. V. Therese. 1995. Essential of Insurance: management. A Risk Management Perspective,43–78. NewYork: John Wiley & Sons. Taehui Kim got the Ph.D from Kyung-hee University, Gurcanli, G. E., S. Bilir, and M. Sevim. 2015. “Activity Based Risk Republic of Korea. Then he is working as a professor in Assessment and Safety Cost Estimation for Residential Mokpo National University, Republic of Korea. His major Building Construction Projects.” Safety Science 80: 1–12. area is construction management. doi:10.1016/j.ssci.2015.07.002. Kiyoung Son got the Ph.D from Texas A&M University then he Highfield, W. E., W. G. Peacock, and S. Van Zandt. 2010. is working as a professor in University of Ulsan, Republic of “Determinants & Characteristics of Damage in Single- Korea. His major area is construction management. Family Island Households from Hurricane Ike1.” The Association of Collegiate Schools of Planning Junseo Bae earned his Ph.D. from Texas A&M University. He is Conference, Minneapolis. currently working as a lecturer (i.e., assistant professor) at the Hsueh, S.-L., Y.-H. Perng, M.-R. Yan, and J.-R. Lee. 2007. “On- University of the West of Scotland, UK. His major area is line Multi-criterion Risk Assessment Model for construction management. Construction Joint Ventures in China.” Automation in Seunghyun Son is a Ph.D. student at Kyung Hee University in Construction 16 (5): 607–619. doi:10.1016/j. Korea, Republic of Korea. His major area is construction autcon.2007.01.001. management. Insurance Statistical Monthbook [Internet]. ([2006] 2015). “Korea insurance development institute.” Accessed 27 july 2016. http://www.insis.or.kr ORCID Kim, C. H., L. S. Kang, and H. T. 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