MétaCan
Menu
Back to cohort
Record W7112795246

Evaluation, design and distribution of sustainable drainage systems in sloping environments

2024· other· en· W7112795246 on OpenAlexaboutno aff

Bibliographic record

VenueThe HKU Scholars Hub (University of Hong Kong) · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSubsoilSurface runoffInfiltration (HVAC)DrainageHydrology (agriculture)Low-impact developmentStorm Water Management ModelWatertable controlStormwater
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.226
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueThe HKU Scholars Hub (University of Hong Kong)French-language works237,207