臺灣博碩士論文加值系統

風險因素必然存在於專案執行過程中之任何一個階段，尤其是對於具動態性
質極高且複雜的營建專案，當然會影響其工期、成本與品質等三個重要績效目標
的預期達成率。因此，完善的風險管理對於營建專案的成功具有關鍵角色，而風險管理程序必需具備風險識別、風險分析與評估、風險回應策略與風險控制等四個重要步驟，其中風險分析與評估更是風險管理中的核心工作。
本研究焦點首先應用結構方程模型(SEM)來發展不同觀點之風險分析模式，包含風險可能發生機率與對專案影響程度的評估模式。之後，再使用層級分析法(Analytic Hierarchy Process, AHP)計算風險構面的權重，並決定重大風險因子之重要性，結合人工神經網路(Artificial Neural Network, ANN)提取施工風險的特徵，以預測重大風險因子與品質之關係。本研究提出此一較新穎且具整體專案觀點的營建專案風險分析模式，可以幫助風險管理者在作相關專案風險回應處理決策時的主要參考依據。

Risk factors must exist at any stage of the project execution process, especially for dynamic a very high-quality and complex construction project will of course affect its three important performance goals: construction period, cost and quality expected achievement rate. Therefore, sound risk management plays a key role in the success of construction projects, and complete Prepared risk management procedures must have risk identification, risk analysis and assessment, risk response strategy and risk control Four important steps including risk management, among which risk analysis and assessment are the core work of risk management.
This research focuses on the application of structural equation modeling (SEM)
to develop risk analysis models from different perspectives, including an assessment model that includes the probability of occurrence of risks and the degree of impact on the project. Using Analytic Hierarchy Process (AHP) calculate the weight of risk facets and determine the importance of major risk factors, combined with artificial neural network (ANN) implement training on construction risks and extract the characteristics of major risk factors to predict major risk factors and quality relationship. This study proposes this relatively novel construction project risk analysis model with a holistic project perspective. It can help the risk manager to make the main reference when making decisions about the risk response of related projects.

中文摘要 ………………………………………………………………i
英文摘要 ………………………………………………………………ii
致謝 ……………………………………………………………………iii
目錄 ……………………………………………………………………iv
表目錄…………………………………………………………………viii
圖目錄 …………………………………………………………………ix
第一章 緒 論 ………………………………………………………01
1.1 研究背景與動機 ……………………………………………01
1.2 研究目的 ……………………………………………………02
1.3 研究內容與步驟 ……………………………………………02
1.4 研究範圍與限制 ……………………………………………03
1.5 研究架構與流程 ……………………………………………04
第二章 文獻回顧 ……………………………………………………06
2.1 風險管理問題在營建專案之探討 …………………………06
2.2 風險管理實施的步驟 ………………………………………07
2.3 SEM 在營建專案風險管理之應用 …………………………08
2.4 AHP 與ANN 在營建專案風險管理之應用 …………………08
2.5 小 結……………………………………………………………10
v
第三章 研究方法 ……………………………………………………11
3.1 結構方程模型方法 …………………………………………12
3.1.1 結構方程模型主要組成部分 ………………………………12
3.1.2 結構方程模型圖示內涵 ……………………………………12
3.1.3 結構方程模型建立步驟 ……………………………………14
3.1.4 結構方程模型機率模式 ……………………………………16
3.2 層級分析法(AHP)……………………………………………16
3.2.1 AHP的主要功能 ……………………………………………17
3.2.2 AHP的層級架構……………………………………………17
3.2.3 AHP的比較矩陣 ……………………………………………18
3.2.4 AHP的數學式 ………………………………………………19
3.3 人工神經網路(ANN)…………………………………………20
3.3.1 ANN 的組成架構與結構……………………………………21
3.3.2 ANN 的運算模型……………………………………………22
3.3.3 ANN 的數學式………………………………………………23
3.4 小結 …………………………………………………………24
第四章 營建專案之風險分析………………………………………26
4.1 資料分析 ……………………………………………………26
4.2 初始營建專案風險管理結構方程模型……………………36
vi
4.2.1 SEM 之應用…………………………………………………36
4.2.2 營建專案施工風險管理SEM 模式之建立…………………37
4.3 最終營建專案風險管理結構方程模型 ……………………44
4.3.1 各構面主要影響風險因素…………………………………44
4.3.2 風險回應處理措施…………………………………………45
4.4 小結……………………………………………………………46
第五章 營建專案施工風險與品質評估…………………………47
5.1 AHP 與ANN 之操作步驟……………………………………48
5.2 施工風險之問卷調查…………………………………………50
5.2.1 問卷樣本整理…………………………………………………51
5.2.2 重大風險因子…………………………………………………52
5.3 營建專案風險構面……………………………………………55
5.3.1 各風險因子權重 ……………………………………………56
5.4 重大風險因子與品質的關聯…………………………………58
5.4.1 ANN 層數架構與參數 ………………………………………58
5.4.2 ANN 準確度與學習誤差…………………………………61
5.5 小結 …………………………………………………………63
第六章結論與建議………………………………………………65
6.1 研究動向……………………………………………………65
vii
6.2 研究結果與建議………………………………………………67
6.3 研究限制與未來研究方向……………………………………68
參考文獻…………………………………………………………70
附錄-1 問卷調查表(A) ………………………………………………81
附錄-2 問卷調查表(B) ………………………………………………85
附錄-3 重大風險因子之權重…………………………………………88

中文部分
1. 丁健昇，2021，探討營造業工安事件防制關鍵因素之研究，國立高雄科技大學，碩士論文。
2. 王衍裕，2012，砂質地盤管幕工法施工之風險分析-以高雄鐵路地下化某隧道工程段標為例砂質地盤管幕工法施工之風險分析-以高雄鐵路地下化某隧道工程段標為例，國立高雄應用科技大學，碩士論文。
3. 吳志鵬，2010，營造業施工安全衛生風險管理過程之研究，國立台灣科技大學，碩士論文。
4. 林建良，羅維，2001，系統動力學與工程品質管制，營建管理季刊第48期第29~36頁。
5. 林建良，范慶龍，2018b，關聯規則挖掘與網路層級分析法於工程等級與主要缺失之研究，中正嶺學報，第48卷，第1期。
6. 林建良，范慶龍，2018a，整合CA和DT兩種資料挖掘方法分析缺失與查核等級之規則，2018第22屆營建工程與管理學術研討會，第152頁，台北。
7. 周慧瑜，2002，營建工程專案承包商風險處置決策模式之研究，國立台灣大學，博士論文。
8. 范慶龍，林建良，2017，以關聯規則與貝氏網路建構軍事工程缺失風險分析模式，第26屆國防科技學術研討會，第51-52頁，桃園。
9. 侯慶豐，2007，建築師参與統包工程風險課題之研究，國立雲林科技大學，碩士論文。
10. 范偉華，2004，統包工程之風險管理，國立朝陽科技大學，碩士論文。71
11. 洪顯宗，2009，捷運工程施工風險評估模式之研究，國立中央大學，碩士論文。
12. 孫守義，2009，工程顧問公司於橋梁工程施工階段專案風險管理之探討，國立交通大學，碩士論文。
13. 高振宏，2009，砂土層中潛盾隧道施工之風險分析－以高雄捷運CO3區段標為例，國立高雄應用科技大學，碩士論文。
14. 原振圖，2007，高架捷運工程施工風險探討，國立高雄第一科技大學，碩士論文。
15. 陳福民，2005，建築物擋土開挖事故風險之分析，國立高雄應用科技大學
16. 陳姿穎，2008，建築工程風險與排程不確定性之探討，國立雲林科技大學，碩士論文。
17. 陳威元，2007，建築工程專案風險評估模式之研究，國立台灣大學，碩士論文。
18. 張權溢，2002，風險評估模式建構之研究，國立台灣科技大學，碩士論文。
19. 黃尚元，2009，營造業者承攬統包工程之風險管理，國立高雄第一科技大學，碩士論文。
20. 楊人能，2002，營建工程聯合承攬之風險管理，國立高雄第一科技大學，碩士論文。
21. 曾英俊，2011，應用模糊層級分析法於扶壁施工之研究，國立台灣科技大學，碩士論文。
22. 詹堯安，2003，設計與施工介面問題之研究－以系統動力學檢視公路工程案例，國立高雄第一科技大學，碩士論文。
23. 廖平啟，2014，營造業承擔公共工程品質風險之研究，國立雲林科技大學，72碩士論文。
24. 鄧明元，2005，系統思考於工程案例分析之初探，國立台灣科技大學，碩士論文。
25. 蔡茂生，2014，營建工程風險管理系統之建置與運用，風險管理土木水利 第四十一卷 第一期。
26. 蕭汎儀，2006，營建風險管理之國外研究趨勢分析與探討，國立交通大學，碩士論文。
27. 穆慧中，2003，營造業施工災害風險分析量化之研究，國立台灣科技大學，碩士論文。
28. 藍原志，2003，專案風險管理回應策略選擇模式之建立，國立中央大學，碩士論文。
29. 羅浩享，2009，營造業職業安全衛生風險管理模式之建構研究，國立台灣科技大學，碩士論文。

英文部分
1. Adeli, H., and Yeh, C., 1989, “Preceptron learning in engineering design”,
Microcomputer in Civil Engineering, vol. 4, pp. 247-256.
2. Adeleke, A.Q., Bahaudin, A.Y., Kamaruddeen, A.M., Bamgbade, J.A. Maruf
Gbadebo Salimon, Muhammad Waris Ali Khan, Shahryar Sorooshian, 2018, “The
Influence of Organizational External Factors on Construction Risk Management
among Nigerian Construction Companies,” Safety and Health at Work 9 115-124.
3. Dziadosz, A., and Rejment, M., 2015, “Risk analysis in construction
project-chosen methods,” Procedia Engineering, 122, 258 – 265.73
4. Nawaz, A., Waqar, A., Shah, S.A.R., Sajid, M., and Khalid, M.I., 2019, “An
Innovative Framework for Risk Management in Construction Projects in
Developing Countries: Evidence from Pakistan”, Risks, 7, 24; doi:10.3390/risks7010024
5. Akintoye, A.S., and MacLeod, M.J., 1997, “Risk analysis and Management in
Construction”, International Journal of Project Management, 15 (1), 31–38. [7]
6. Alex, D. P., Al Hussein, A., M., Bouferguene, A., and Fernando, S., 2010,
“Artificial neural network model for cost estimation: City of Edmonton’s water
and sewer installation services”, Journal of Construction Engineering and
Management, 136(7), 745-756.
7. Mills, A., 2001, “A systematic approach to risk management for construction”,
Structure survey”, RI=L*C 8. Attalla, M., and Hegazy, T., 2003, “Predicting cost deviation in reconstruction projects: Artificial neural networks versus regression”, Journal of Construction Engineering and Management, 129(4), 405-411.
9. Ayhan, B.U., and Tokdemir, O.B., 2020, “Accident analysis for construction safety using latent class clustering and artificial neural networks”, Journal of Construction Engineering and Management, 146(3), 04019114.
10. Bai, L., Wang, Z., Wang, H., Huang, N., and Shi, H., 2020, “Prediction of
multiproject resource conflict risk via an artificial neural network”, Engineering, Construction and Architectural Management.
11. Bentler, P.M., 1990, “Comparative fit indexes in structural models,” Psychological Bulletin, Vol.107, No.2, pp. 238-246 (1990). 74
12. Bu-Qammaz, A.S., Dikmen, I., and Birgonul, M.T., 2009, “Risk assessment of
international construction projects using the analytic network process”, Canadian Journal of Civil Engineering, Vol.36(7), pp. 1170-1181.
13. Byrne, B.B., 2010, Structural Equation Modeling with AMOS: Basic Concepts,
Applications, and Programming, 2nd ed.; Routledge: New York, NY, USA, ISBN
978-0-8058-6373-4.
14. Carmer, S.G., and Swanson, M.R., 1971, “Detection of differences between means: A Monte Carlo study of five pair-wise multiple comparison procedures”, Agron. J. 63:940-945.
15. Cascardi, A., Micelli, F., and Aiello, M.A., 2017, “An artificial neural networks model for the prediction of the compressive strength of FRP-confined concrete circular columns”, Engineering Structures, vol. 140, pp. 199-208.
16. Cerić, A., and Marić, T., 2011, “Determining priorities for managing risk on
construction projects”, Građevinar, 63(03.), 265-271.
17. Kaushaly, C., Fernando, M., Reza Hosseini, Edmundas Kazimieras Zavadskas,
B.A.K.S. Perera, Raufdeen Rameezdeen, 2017, “Managing the Financial Risks
affecting ConstructionContractors: Implementing Hedging in Sri Lanka”,
International Journal of Strategic Property Management, Volume 21(2):
212-224,(2017).
18. Choudhry, R., and Iqbal, K., 2013, “Identification of risk management system in construction industry in Pakistan”, Journal of Management in Engineering, 29(1): 42–49.
19. Cronbach, L.J., 1951, “Coefficient Alpha and the internal structure of tests”, 75 Psychometrika, Vol.16, pp. 297-334.
20. Douglas, Hubbard, 2009, “The Failure of Risk Management: Why it’s Broken and
How to Fix It”. J.Wiley & Sons, NY, 46.11.[3]
21. Ehsani, E., Kazemi, N., Olugu, E.U., Grosse, E.H., and Schwindl, K., 2017,
“Applying fuzzy multi-objective linear programming to a project management
decision with nonlinear fuzzy membership functions”, Neural Computing and
Applications, 28(8), 2193-2206.
22. El-Sayegh, S.M., and Mansour, M.H., 2015, “Risk assessment and allo cation
inhighway construction projects in the UAE”, Journal of Management in
Engineering, 31(6), 04015004.
23. Fan, C.L., 2020, “Application of the ANP and fuzzy set to develop a construction quality index: A case study of Taiwan construction inspection”, Journal of Intelligent & Fuzzy Systems, 38(3), 3011-3026.
24. Fan, M., Lin, N.P., and Sheu, C., 2008, “Choosing a project risk-handling strategy: an analytical model”, International Journal of Production Economics, 112, 700–713.[2][7]
25. Flanagan R., Norman G., and Chapman R., 2006, Risk Management and
Construction, 2nd ed.Oxford: Blackwell Pub; (2006).
26. Fornell, C., and Larcker, D.F., 1981, “Evaluating structural equation models with unobservable variables and measurement error,” Journal of Marketing Research, Vol.18, No.1, pp.39-50 (1981).
27. Ghassemieh, M., and Nasseri, M., 2012, “Evaluation of stiffened end-plate
moment connection through optimized artificial neural network.” J. Softw. Eng.
76 Appl., 5, 156–167. https://doi.org/10.4236/jsea.2012.53023.
28. Hoyle, R.H., 1995, The Structural Equation Modeling Approach: Basic Concepts
and Fundamental Issues, In R. H. Hoyle (Ed.), structural equation modeling
concepts, issues and applications (pp.1-15). Thousand Oaks, CA: Sage.
29. Jafarzadeh, R., Ingham, J.M., Wilkinson, S., González, V., and Aghakouchak,
A.A., 2014, “Application of artificial neural network methodology for predicting
seismic retrofit construction costs”, Journal of Construction Engineering and
Management, 140(2), 04013044.
30. Ji, C., Su, X., Qin, Z., and Nawaz, A., 2022, “Probability Analysis of Construction Risk based on Noisy-or Gate Bayesian Networks”, Reliability Engineering & System Safety, 217, 107974.
31. Jin, J., Li, Z., Zhu, L., Tong, X.H., and Yang, C.W., 2019, “Application of BP neural network in risk evaluation of railway construction”, Journal of Railway Engineering Society, 3, 103-109.
32. Kartam, N.A., and Kartam, S.A., 2001, “Risk and its management in the Kuwaiti construction industry: A contractor’s perspective”, International Journal of Project Management, 19, 325–335.
33. Kim, T., 2010, Pattern Recognition Using Artificial Neural Network: A Review, In Y. X. Samir Kumar Bandyopadhyay, Wael Adi, Tai-hoon Kim (Ed.), Information Security and Assurance (pp. 138–148). Springer, Berlin, Heidelberg.
https://doi.org/10.1007/978-3-642-13365-7_14
34. Kline, R. B., 2001, Principles and Practice of Structural Equation Modeling, (3nd ed.). New York: The Guilford Publications, Inc.77
35. Krizhevsky, A., Sutskever, I., and Hinton, G.E., 2012, “Imagenet classification with deep convolutional neural networks”, Advances in Neural Information Processing Systems, 25, 1097-1105.
36. Park, K., Lee, S., and Ahn, Y., 2017, “Construction Management Risk System
(CMRS) for Construction Management (CM) Firms”, Future Internet; doi:10.3390/fi9010005
37. Lu, M., and Wudhikarn, R. 2022, “Using the best-worst method to
developintellectual capital indicators in financial service company”, In 2022 Joint International Conference on Digital Arts, Media and Technology with ECTI
Northern Section Conference on Electrical, Electronics, Computer and
Telecommunications Engineering (ECTI DAMT & NCON) (pp. 81-86). IEEE.
38. Tipili, L.G., and Yakubu, I., 2016, “Identification and assessment of key risk factors affecting public construction projects in nigeria: stakeholders perspectives”, International Journal of Engineering and Advanced Technology Studies, Vol.4, No.2, pp.20-32,(2016).
39. Maqsoom, A., Babar, Z., Shaheen, I., Abid, M., Kakar, M.R., Mandokhail, S.J., et al. “Influence of construction risks on cost escalation of highway-related projects: exploring the moderating role of social sustainability requirements”, Iranian Journal of Science and Technology, Transactions of Civil Engineering. 2021:1-13.
40. Kaur, M., and Singh, R., 2018, “Risk and Risk-Handling Strategies in Construction Projects”, International Journal of Management Studies, (2018).
41. Marsh, H. W., and Hau, K.T., 1999, Confirmatory Factor Analysis: Strategies for Small Sample Sizes, In R. H. Hoyle (Ed.), Statistical strategies for small sample 78 research, pp. 252-284, Thousand Oaks, CA: Sage
42. Mir, M., Kabir, H.D., Nasirzadeh, F., and Khosravi, A., 2021, “Neural
network-based interval forecasting of construction material prices”, Journal of
Buildin Engineering, 39, 102288.
43. M. Lu, S. M. AbouRizk and U. H. Hermann, “Sensitivity analysis of neural
networks in spool fabrication productivity studies”, J. Comput. Civil Eng., vol. 15,no. 4, pp. 299-308, Oct. 2001.
44. Modarres, M. 2006, Risk Analysis in Engineering-techniques, Tools, and Trends.” 1st ed. Boka Raton: CRC Press; (2006).
45. Algahtany, M., Alhammadi, Y., and Kashiwagi, D., “Introducing a New Risk
Management Model to the Saudi Arabian Construction Industry”, Procedia
Engineering, (2016) (145) pp.940 – 947.
46. Abazid, M., and Harb, H., 2018, “An overview of risk management in the
construction projects”, Academic Research International, Vol. 9(2) June (2018).
47. Ehsan, N., Alam, M., Mirza, E., and Ishaque, A., 2010, “Risk Management in
construction industry”, The 3rd IEEE International Conference on Computer
Science and Information Technology, (2010).
48. Naderpour, H., Rafiean, A.H., Fakaharian, P., 2018, “Compressive strength
prediction of environmentally friendly concrete using artificial neural networks”, Journal of Building Engineering, vol. 16, pp. 213-219.
49. Park, J.K., Hossain, S., Oh, J., Yoo, H., and Kim, H., 2020, “Assessment of risk potential due to underground box structure installation employing ANN model and field experimental approaches”, Journal of Performance of Constructed 79
Facilities, 34(4), 04020057.
50. Kishan, P., and Bhatt, R., 2014, “A Study of Risk Factors Affecting Building
Construction Projects”, International Journal of Engineering Research and
Technology, Vol. 3 Issue 12, December (2014).
51. Pereira, E., Ali, M., Wu, L., and Abourizk, S., 2020, “Distributed
Simulation–Based Analytics Approach for Enhancing Safety Management Systems
in Industrial Construction”, Journal of Construction Engineering and
Management, 146(1), 04019091.
52. Prasad, B.R., Eskandari, H., Reddy, B.V., 2009, “Prediction of compressive
strength of SCC and HPC with high volume fly ash using ANN”, Constr Build
Mater, 23:117–28.
53. Prasanta Kumar Dey, “Project Risk Management: A Combined Analytic Hierarchy
Process and Decision Tree Approach”, Cost Engineering, Vol. 44/No. 3 MARCH
(2002).
54. Rehacek, P., 2017, “Application and usage of the standards for project management and their comparison”, J. Eng. Appl. Sci., 12: 994-1002.
55. Barber, R., and Burns, M., 2002, “A Systems Approach to Risk Management”,
ANZ Systems (2002), Mooloolaba, 10 Dec 2002.
56. Rigdon, Edward E., Structural Equation Modeling: Nontraditional Alternatives, In Brian Everitt and David Howell (eds.), Encyclopedia of Statistics in Behavioral
Science. New York: Wiley. (2005).
57. Rostami, A., and Oduoza, C.F., 2017, “Key risks in construction projects in Italy: contractors’ perspective”, Engineering, Construction and Architectural
80 Management.
58. Saaty, T.L., 1980, The Analytic Hierarchy Process, New York: McGraw-Hill.
59. Schumacker, R.E., and Lomax, R.G., 2004, A Beginner’s Guide to Structural
Equation Modeling, (2nd ed.). Mahwah, NJ: Erlbaum.
60. SLAC National Accelerator Laboratory, 2009, “Risk Management Plan”, Research
Support Building and Infrastructure Modernization, LAC‐I‐050‐07010‐002, April
(2009).
61. Viswanathan, S.K., and Jha, K.N., 2020, “Critical risk factors in international construction projects: An Indian perspective”, Engineering, Construction and Architectural Management,
62. Vlăduț-Severian IACOB, “Risk management and evaluation and qualitative
method within the projects”, ECOFORUM ,Volume 3, Issue 1 (4), (2014).
63. Wu, L., Bai, H., Yuan, C., and Xu, C., 2019, “FANPCE technique for risk
assessment on subway station construction”, Journal of Civil Engineering and
Management, 25(6), 599-616.
64. Yan, F., Lin, Z.B., Azarmi, F., and Sobolev, K., 2017, “Evaluation and prediction of bond strength of GFRP-bar reinforced concrete using artificial neural network optimized with genetic algorithm”, Composite Structures, vol. 161, pp. 441-452.
65. Zavadskas, E.K., Turskis, Z., and Tamošaitiene, J., 2010, Risk assessment of
construction projects. Journal of Civil Engineering and Management, 16(1), 33-46.