Evaluation, design and distribution of sustainable drainage systems in sloping environments
Bibliographic record
Abstract
A sustainable drainage system (SuDS) is a collection of water management practices that aim to align modern drainage systems with natural water processes. The primary goal of SuDS is to manage stormwater and improve water quality through runoff collection, infiltration, storage, and exfiltration, thereby mitigating the effects of urbanization on natural water cycles. However, implementing SuDS in sloping environments is challenging. First, slopes reduce infiltration rates and runoff reduction capacity in SuDS, challenging the system’s effectiveness. This thesis explored the design of stepped SuDS on slopes through physical and numerical models, focusing on the effects of changes in cell quantity and pipe installation on hydrologic outcomes and water distribution. Results revealed that fewer cells and underdrains enhance peak runoff management, whereas connecting the storage layer of each cell causes unbalanced infiltration among cells. The study demonstrated that stepped SuDS can effectively manage runoff, achieving up to 97% volume reduction, highlighting their suitability for sloping areas. Second, slopes hinder the effective diversion of runoff into SuDS that are not situated in depressions. Toronto exfiltration systems (TES), a type of SuDS, present feasible solutions by integrating with existing conventional drainage systems designed for runoff collection. This thesis investigated the hydrological performance of TES on sloped streets with varying angles, under diverse site conditions including rainfall patterns and subsoil types. Results indicated that TES is highly effective on slopes, achieving nearly 100% runoff reduction for rainfall with a 2-year return period. Moreover, steeper slopes facilitate rapid groundwater mound dissipation, especially when the subsoil consists of loamy sand. Third, slopes face the risk of failure with the introduction of SuDS as the implementation of the system alters the surface topography and subsurface hydrology. To reveal the effects, a numerical model simulated the hydrological and geotechnical impacts of a two-stepped bioretention cell system on slopes of 15°, 20°, and 25°, with varying groundwater levels. Findings revealed that while slope modifications can increase stability, groundwater mounds from SuDS exfiltration might decrease the safety factor (SF) by approximately 0.2. SuDS practices are deemed generally safe for 15° slopes. Fourth, slopes challenge the wide distribution of SuDS in landslide-prone catchment. This study investigated SuDS' effectiveness, risks, and distribution strategies in such terrains using SWMM and Modflow for hydrologic simulations and Scoops3D for slope stability analysis. It compared uniform, slope-away, and far slope-away distributions at various implementation ratios. Results showed SuDS effectively reduce runoff and enhance groundwater storage, with slope-away strategies optimizing both groundwater replenishment and slope stability. While low implementation levels minimally impact stability, higher ratios suggest slope-away methods best balance hydrological benefits with reduced landslide risks. In conclusion, stepped SuDS and TES are an effective form or type of SuDS practices well-suited for implementation on slopes. Slopes less than 15° are deemed safe for such installations. A strategic distribution, notably the slope-away distribution, facilitates the application of SuDS in areas prone to landslides, offering a viable solution for enhancing slope stability while managing runoff effectively.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".