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Record W4383957461 · doi:10.24928/2023/0149

Promoting the IPD Delivery Method in Construction Projects: A BIM-Based Smart Contract Approach

2023· article· en· W4383957461 on OpenAlexaff
Mohamed Assaf, Lena Salami, Diana Salhab, Ahmed Hammad

Bibliographic record

VenueAnnual Conference of the International Group for Lean Construction · 2023
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIntegrated project deliveryConstruction engineeringComputer scienceSmart contractProcess managementSystems engineeringEngineering managementBusinessEngineeringComputer securityProject managementBlockchain

Abstract

fetched live from OpenAlex

The adoption of integrated project delivery (IPD) provides several advantages over traditional delivery methods, such as shorter schedules, efficient communication, and higher performance quality.However, its implementation is constantly hindered by many barriers.Existing studies on IPD barriers are limited to quantifying and addressing such obstacles.Additionally, hardly any studies have addressed the potential of advanced technologies in exploiting the adoption of IPD projects.Thus, this study presents an automated system that integrates blockchain, smart contracts, and BIM technologies to facilitate the implementation of IPD projects.Hyperledger Fabric and chaincodes are used to develop the blockchain network in accordance with 4D and 5D BIM models.The developed system simplifies various financial transactions throughout different phases of the IPD project implementation.The system allows non-owner participants to submit requests and review transaction records with the aim of minimizing possible conflicts.The methodology is evaluated by testing it on a real-life case study.The case study is modeled using BIM tools, and the corresponding blockchain network and smart contracts are developed.The findings prove the capability of the developed system to provide a secure and trustworthy platform for managing IPD transactions without the need for third-party involvement.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.931
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.249
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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