A Strategic Design Approach for Implementing Rainwater Management System Using an Integration of GIS and BIM Tool
Bibliographic record
Abstract
An increase in urbanization and uncontrolled development has resulted in a water stress situation, which necessitates the exploration of alternate water sources. Rainwater has proven to be a prominent alternate water source after being efficiently harvested. On-ground implementation of the Rainwater Harvesting System (RWHs) at a community level in urban areas has always been challenging and requires technological advancement. To facilitate the implementation of RWHs, the proposed study provides a comprehensive methodology by integrating the Geographical Information System (GIS) and the Building Information Modeling (BIM) tools. Initially, the hydro-spatial analysis was performed with a GIS tool to obtain an optimized rainstorm collection network and to aid in establishing the geometrical properties of RWHs. Further, an outcome from the analysis was utilized to develop a visualization model using the BIM tool. The proposed methodology is implemented as a case study in the municipality of Jaipur (India). The developed multidimensional BIM contributes to the sustainability of the project in terms of resources, economy, and efficiency over the life cycle. As an outcome, the proposed study provides a comprehensive methodology for effectively utilizing rainwater to cope with the growing water demand and contribute to flood mitigation in urban regions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".