Chen, G (2023) Enhance collaboration reliability at task level for construction projects using blockchain technology. PhD thesis, North Carolina State University, USA.
Abstract
Reliable workflow is critical in enhancing the construction industry’s overall productivity. A reliable workflow can be achieved by ensuring that communication flows smoothly between all specialty trades involved, constraints are removed, and instant feedback is available. Moreover, a transparent and accountable environment can uphold a reliable workflow because all parties can understand their responsibilities, risks, and expected outcomes unambiguously. Recently, the adoption of blockchain technology in construction has emerged as a disruptive trend, providing opportunities to facilitate and decentralize production management through the power of smart contracts. However, the following questions are yet to know: (1) how to investigate the impacts of constraints arising from the specialty trades’ variations in terms of project outcomes, (2) how to derive fair profit allocation contract consensus among specialty trades when working in the coalition, and (3) how to integrate semantically rich as-built data to provide automatic project appraisals and instant decision-making to enhance a reliable workflow. This research aims to provide contextualization, incentivization, and digitalization to enhance task-level collaboration reliability using blockchain-enabled smart contracts. Three primary goals are to (1) quantify the interdependent interrelated impacts of project constraints arising from variations in specialty trades on project outcomes, (2) derive a fair benefit distribution method among specialty trades when working in a collaborative work setting, and (3) integrate semantically rich as-built images to automate smart contract executions.First, the research selected a modular construction project to investigate the impact of specialty trades’ variations on the project's duration and costs. A simulation model was deployed, and the resulting profits were encoded in smart contracts as incentive-penalty sharing rules. These rules were designed to enforce a higher level of performance among specialty trades, thereby ensuring reliable constraint removal. To record and share as-built images as unique evidence for performance verification, an InterPlanetary File System (IPFS)-based approach was integrated into the framework. Next, the research combined and permutated various collaborative variables for three specialty trades, generating 27 scenarios in simulation. The Shapley value was then applied to aggregate the outcomes and determine the fair benefit-sharing mechanism, which was subsequently embedded in smart contracts and tested in various scenarios to validate the effectiveness and efficiency of the developed framework. Lastly, a high-rise residential building was selected, and as-built image data was collected. To classify the as-built image into different progress categories, a deep learning model was developed for blockchain oracle development. Furthermore, the research analyzed the various impacts of prerequisite delays in-depth and identified optimal mitigation strategies. Ultimately, a decentralized application was developed, which synchronizes as-built images, classified results, and mitigation strategies to enable automatic smart contract executions.The first case study revealed that reliable constraint removal leads to substantial reductions in wait time for equipment and labor, resulting in an overall cost savings of 4.7% compared to the benchmark scenario. For example, the removal of material constraints resulted in a 45.7% and 78.5% reduction in equipment and labor wait times, respectively. Additionally, the use of simulation to convert implicit construction dynamics into explicit contract consensus facilitated the adoption of smart contracts in the construction industry.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Hsiang, S; Han, K; Jaselskis, E; Fang, S-C and Liu, M |
| Uncontrolled Keywords: | blockchain; case study; collaboration; communication; duration; equipment; feedback; incentivization; learning; modular construction; productivity; reliability; residential; simulation; variations; workflow |
| Index terms: | case study, efficiency, deep learning, project outcome, aggregate, productivity, high-rise residential building, effectiveness, dynamics, blockchain, cost saving, duration, construction industry, smart contract, digitalization, production management, penalty, profit, project appraisal, strategy, project constraint, evidence, mitigation, collaboration, construction project, modular construction, decision-making, variation, incentivization, workflow |
| Subjects: | risk assessment, data collection methods, control systems, computing systems, project delivery, artificial intelligence, performance management, economics, management, contractual condition, decision analysis, project controls, systems engineering, industry analysis, regulatory law, project completion, construction type, evaluation and assessment methods, materials science, digital technology, economic analysis, financial risk, production management, building construction |
| Topics: | Project Management, Engineering Principles, Risk Management, Quality Management, Legal Issues, Research Practice, Business Strategy, Cost Management, Construction Technology, Digital Applications, Time Control, Contract Administration, Organizational Design |
| Descriptive scope: | 5 PCTEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here