Advancing Construction Planning in Remote Regions: A Stochastic Time-Window Framework for Schedule and Cost Efficiency
Bibliographic record
Abstract
Construction planning in remote regions is significantly challenged by stochastic time-window constraints induced by environmental and logistical factors. These constraints, such as limited access (e.g., no physical road), unpredictable weather (e.g., typhoons, blizzards, shifting freeze-thaw cycles, sand storms), and regulatory nature-based restrictions (e.g., season-based ecosystem protection regulations), introduce uncertainties that traditional construction planning methods like the Critical Path Method or Time-Cost Trade-off analysis are not designed to accommodate effectively. As a result, plans generated by such techniques are often flawed and infeasible, leading to persistent delays and cost overruns in projects undertaken in harsh, remote environments. Addressing these challenges is critical for infrastructure development, which has a socio-economic impact on remote communities. Furthermore, in the mining industry, often involving distant operations, the efficiency and feasibility of natural resource extraction can also be impacted by environmental and logistical constraints. Evidently, attempting the execution of construction projects in such settings almost certainly involves schedule delays and budget overruns. As traditional planning methods fall short, the demand for a robust strategy to tackle environmental uncertainties is especially evident in the context of climate change and unpredictable natural phenomena. A stochastic time-window framework has been developed to enhance construction planning and scheduling in remote settings. This framework integrates environment-induced constraints into the scheduling process, optimizes the selection of alternative execution methods for environment-sensitive activities, and quantifies the impact of stochastic time windows on project duration and total cost, thus addressing the shortcomings of conventional planning and cost optimization techniques. Initially, a Time-Window planning method was formulated to incorporate environmental constraints, validated via a case study of a river-crossing bridge in a remote northern region of Canada. Subsequently, a quantitative optimization framework was developed, employing enumerated simulation and an analytical reward function ranking alternative execution methods and crew configurations. Finally, a simulation-based approach utilizing the Monte Carlo method and historical meteorological and winter road access data was implemented to model the stochastic nature of environmental time windows for a project situated in northern Canada. The findings of this dissertation indicate that the proposed framework substantially improves schedule reliability over traditional methods in the settings of logistically complex and climate-sensitive regions. The Time-Window planning method ensures practical construction scheduling under environmental constraints, while the time-cost optimization framework identifies effective execution alternatives in parallel. Finally, the stochastic simulation of weather-related constraints reveals a likelihood of delays and budget overruns when compared to baseline estimates based on average records. These findings suggest that the stochastic time-window framework can be adopted as a valuable tool by construction planners in remote regions. By enabling more accurate scheduling, cost management, and risk budgeting, the framework enhances the feasibility of construction projects in geographically and environmentally challenging areas and advances the field of construction engineering and management.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".