Synergising Lifecycle Project Management for Sustainability: Towards a Streamlined Approach through different Project Phases
Bibliographic record
Abstract
The Architecture, Engineering, and Construction (AEC) industry is transforming project management, departing from traditional linear methodologies.At the forefront of this evolution is the Total Life Cycle Process (TLCP), a paradigm that redefines project management throughout the project lifecycle.Adhering to DIN EN ISO 19650 principles, this innovative methodology utilizes database-supported information models (dIMs), marking a substantial leap towards a fully digital project management system.Unlike conventional methods, TLCP transcends procedural changes by systematically deriving information requirements.This is achieved through meticulous analysis of Information Requirement Matrices (IRMs), tailored to address unique use cases in each project phase.This strategic approach not only enhances communication but also ensures stakeholders have timely access to relevant information, fostering efficiency and collaboration.Critical evaluation and revision of linear processes follow IRM analysis, forming the basis for a robust transition to an agile model.This addresses challenges in project phase transitions, laying the groundwork for a more efficient and integrated project management framework.The overarching goal is to present a cross-phase methodology optimizing resource utilization and adapting to technological advances in the dynamic AEC landscape.The methodology facilitates resource-efficient structure design, construction, operation, and decommissioning.The paper introduces a digital TLCP based on the Level of Information Need (LOIN) framework, embodying the key factors and serving as a practical demonstration.By presenting this digital TLCP, the authors aim to stimulate discussion within the AEC industry, contributing to the evolution of sustainable construction practices.In conclusion, this paper serves as a catalyst for a broader discussion, inviting AEC stakeholders to engage in the ongoing evolution of project management practices.The TLCP approach, emphasizing integration, sustainability, and adaptability, represents a significant stride towards efficiently managing construction projects for a more sustainable and resilient built environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".