Decision-making for Cross-Laminated Timber Modular Construction Logistics Using Discrete Event Simulation
Bibliographic record
Abstract
Modular construction is being touted as one solution to address project delays and cost overruns in the construction industry. Modular construction is a delivery method wherein building components are prefabricated off-site and then transported to the job site for assembly. Thus, prefabrication is a significant element of modular construction that enables work to happen in parallel to accelerate project schedules, enhance safety, and reduce physical work on-site. Timber is becoming a primary material for prefabricating elements since wood is a renewable material, possesses high strength-weight ratio, and sequesters carbon. The use of wood in the form of cross-laminated timber (CLT) introduces new opportunities but also logistical issues in the supply chain from forest to the manufacturing facility to the construction site. Depending on the type of CLT and the level of modularity (i.e., 2D elements or volumetric), major constraints in this process have been identified including (1) fluctuation in the supply of raw wood to manufacturing facilities, (2) limitations in the capacity to create CLT panels, (3) shipping limitations based on allowable loads, and (4) crane availability for assembly of panels on the site. This paper explores the use of simulation models to study the effect of these logistical constraints in modular construction using prefabricated CLT on the total time and hence cost of projects. Specifically, discrete event simulation (DES) will be used to model CLT logistics to identify bottlenecks and provide sensitivity analyses of variables such as lumber supply, travel times, and manufacturing plant capacity on project cost and time. A case study of modular multi-story building construction is examined to showcase the utility of the developed simulation framework. It is expected that simulating modular CLT logistics will enable the identification of optimal strategies towards their successful implementation.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".