Bayesian belief network-based project complexity measurement considering causal relationships
This research proposes a Bayesian belief network-based approach to measure the project complexity in the construction industry. Firstly, project complexity nodes are identified for model development based on the literature review. Secondly, the project complexity measurement model is developed with...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/145597 |
| _version_ | 1811686713236389888 |
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| author | Luo, Lan Zhang, Limao Wu, Guangdong |
| author2 | School of Civil and Environmental Engineering |
| author_facet | School of Civil and Environmental Engineering Luo, Lan Zhang, Limao Wu, Guangdong |
| author_sort | Luo, Lan |
| collection | NTU |
| description | This research proposes a Bayesian belief network-based approach to measure the project complexity in the construction industry. Firstly, project complexity nodes are identified for model development based on the literature review. Secondly, the project complexity measurement model is developed with 225 training samples and validated with 20 test samples. Thirdly, the developed measurement model is utilized to conduct model analytics for sequential decision making, which includes predictive, diagnostic, sensitivity, and influence chain analysis. Finally, EXPO 2010 is used to testify the effectiveness and applicability of the proposed approach. Results indicate that (1) more attention should be paid on technological complexity, information complexity, and task complexity in the process of complexity management; (2) the proposed measurement model can be applied into practice to predict the complexity level for a specific project. The uniqueness of this study lies in developing project complexity measurement model (PCMM) with the cause-effect relationships taken into account. This research contributes to (a) the state of knowledge by proposing a method that is capable of measuring the complexity level under what-if scenarios for complexity management, and (b) the state of practice by providing insights into a better understanding of causal relationships among influencing factors of complexity in construction projects. |
| first_indexed | 2024-10-01T05:04:47Z |
| format | Journal Article |
| id | ntu-10356/145597 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:04:47Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1455972020-12-30T02:01:43Z Bayesian belief network-based project complexity measurement considering causal relationships Luo, Lan Zhang, Limao Wu, Guangdong School of Civil and Environmental Engineering Engineering::Civil engineering Project Complexity Measurement Model Bayesian Belief Network This research proposes a Bayesian belief network-based approach to measure the project complexity in the construction industry. Firstly, project complexity nodes are identified for model development based on the literature review. Secondly, the project complexity measurement model is developed with 225 training samples and validated with 20 test samples. Thirdly, the developed measurement model is utilized to conduct model analytics for sequential decision making, which includes predictive, diagnostic, sensitivity, and influence chain analysis. Finally, EXPO 2010 is used to testify the effectiveness and applicability of the proposed approach. Results indicate that (1) more attention should be paid on technological complexity, information complexity, and task complexity in the process of complexity management; (2) the proposed measurement model can be applied into practice to predict the complexity level for a specific project. The uniqueness of this study lies in developing project complexity measurement model (PCMM) with the cause-effect relationships taken into account. This research contributes to (a) the state of knowledge by proposing a method that is capable of measuring the complexity level under what-if scenarios for complexity management, and (b) the state of practice by providing insights into a better understanding of causal relationships among influencing factors of complexity in construction projects. Published version 2020-12-30T02:01:43Z 2020-12-30T02:01:43Z 2020 Journal Article Luo, L., Zhang, L., & Wu, G. (2020). Bayesian belief network-based project complexity measurement considering causal relationships. Journal of Civil Engineering and Management, 26(2), 200-215. doi:10.3846/jcem.2020.11930 1392-3730 https://hdl.handle.net/10356/145597 10.3846/jcem.2020.11930 2 26 200 215 en Journal of Civil Engineering and Management © 2020 The Author(s). Published by VGTU Press. 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 unre-stricted use, distribution, and reproduction in any medium, provided the original author and source are credited. application/pdf |
| spellingShingle | Engineering::Civil engineering Project Complexity Measurement Model Bayesian Belief Network Luo, Lan Zhang, Limao Wu, Guangdong Bayesian belief network-based project complexity measurement considering causal relationships |
| title | Bayesian belief network-based project complexity measurement considering causal relationships |
| title_full | Bayesian belief network-based project complexity measurement considering causal relationships |
| title_fullStr | Bayesian belief network-based project complexity measurement considering causal relationships |
| title_full_unstemmed | Bayesian belief network-based project complexity measurement considering causal relationships |
| title_short | Bayesian belief network-based project complexity measurement considering causal relationships |
| title_sort | bayesian belief network based project complexity measurement considering causal relationships |
| topic | Engineering::Civil engineering Project Complexity Measurement Model Bayesian Belief Network |
| url | https://hdl.handle.net/10356/145597 |
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