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Towards Construction’s Digital Future: A Roadmap for Enhancing Data Value
The success of a construction project depends on the execution of numerous project management functions by multiple stakeholders. While digitalization and ‘big data’ solutions have enhanced practice across many sectors, the construction industry has failed to capitalize on such advances. Fragmentation along project value chains, project complexity and uncertainty, transience of involved stakeholders, and, often, remote and harsh environments have obstructed the development of a standardized digital solution in construction at both the industry and corporate levels. As a consequence, construction data remain noisy, fragmented, and heterogeneous (e.g. both subjective and observational, both structured and unstructured). These types of data form natural barriers for use in data-driven applications, inhibiting the use of data-driven decision-support systems in construction. Aimed at bridging raw construction data with real-time data-driven applications, this research reviewed state-of-the-art information technologies in construction literature and industrial practice to identify three challenges limiting digitalization in construction: (1) heavy manual data manipulation when pre-processing raw construction data for project-level decision-support, (2) low implementation of machine learning to appropriately deal with the flood of available construction data, and (3) a lack of means for fusing heterogeneous information derived from various sources for data-driven simulation in real-time. Following the exploration and adaptation of a number of interdisciplinary methods, this research addressed the identified challenges by providing a novel roadmap for improving the transformation of fragmented construction data into reliable, real-time, and data-driven decision-support.
Towards Construction’s Digital Future: A Roadmap for Enhancing Data Value
The success of a construction project depends on the execution of numerous project management functions by multiple stakeholders. While digitalization and ‘big data’ solutions have enhanced practice across many sectors, the construction industry has failed to capitalize on such advances. Fragmentation along project value chains, project complexity and uncertainty, transience of involved stakeholders, and, often, remote and harsh environments have obstructed the development of a standardized digital solution in construction at both the industry and corporate levels. As a consequence, construction data remain noisy, fragmented, and heterogeneous (e.g. both subjective and observational, both structured and unstructured). These types of data form natural barriers for use in data-driven applications, inhibiting the use of data-driven decision-support systems in construction. Aimed at bridging raw construction data with real-time data-driven applications, this research reviewed state-of-the-art information technologies in construction literature and industrial practice to identify three challenges limiting digitalization in construction: (1) heavy manual data manipulation when pre-processing raw construction data for project-level decision-support, (2) low implementation of machine learning to appropriately deal with the flood of available construction data, and (3) a lack of means for fusing heterogeneous information derived from various sources for data-driven simulation in real-time. Following the exploration and adaptation of a number of interdisciplinary methods, this research addressed the identified challenges by providing a novel roadmap for improving the transformation of fragmented construction data into reliable, real-time, and data-driven decision-support.
Towards Construction’s Digital Future: A Roadmap for Enhancing Data Value
Lecture Notes in Civil Engineering
Walbridge, Scott (Herausgeber:in) / Nik-Bakht, Mazdak (Herausgeber:in) / Ng, Kelvin Tsun Wai (Herausgeber:in) / Shome, Manas (Herausgeber:in) / Alam, M. Shahria (Herausgeber:in) / el Damatty, Ashraf (Herausgeber:in) / Lovegrove, Gordon (Herausgeber:in) / Wu, L. (Autor:in) / AbouRizk, S. (Autor:in)
Canadian Society of Civil Engineering Annual Conference ; 2021
Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021 ; Kapitel: 17 ; 225-238
30.05.2022
14 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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