Scheduling tools for the construction industry: overview and decision support system for tool selection
Bibliographic record
Abstract
The construction industry is a major sector of employment but it has been lagging behind other sectors in terms of productivity for years. Better planning and a heightened presence of technology are advised to reduce the productivity gap. This article aims to combat the haphazard manual process involved in building-erection planning and the associated lag in productivity growth by pointing industry stakeholders to the tools suited for their needs. It may also serve as a basis for further academic research on construction automation, by presenting all the tools found in a uniform and objective structure. To achieve these goals, a systematic literature review of industry-related articles published between January 2008 and 2019 was conducted, leading to the identification of 31 computerized scheduling tools developed specifically for the construction sector. Through this process, trends such as the most widely used software, the countries of origin, the methods of fabrication and the level of automation were identified. The review also resulted in a classification that was later validated via semi-structured interviews with members of the construction industry. Following these interviews, a decision support system was created to facilitate the selection of the tools depending on the planning requirements to address. This will allow project managers to access a wide range of tools and select the ones that best fit their needs. With automated schedule delivery and resource planning, security risks warnings or 4D visualization, project managers can find in those tools an edge that will lead to better working practices and results.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".