QUALITY MANAGEMENT PRACTICES IN GONDRANG-PULCHOWK SIX-LANE ROAD UPGRADING PROJECT AT CHITWAN DISTRICT, NEPAL
Bibliographic record
Abstract
The objective of this study is to assess the current quality management practices, determine the factors affecting quality management and to find out remedial measures that will improve the quality management practices in Gondrang-Pulchowk six-lane road construction project.This research methodology is based on interviews and questionnaires survey with clients and contractors who were involved in the project as well as experts from related field. Statistical tools, relative importance index, validity and reliability tests, Spearman’s correlation tests were used for the ranking of different quality aspects.From the comparative study, it was found that most guidelines of the General Conditions of Contract, Special Specifications and Quality Assurance Plan of the contract document were met during project execution although few concerns of safety and correspondence remain to be addressed.From the questionnaire survey, it was found that the practices to achieve quality need to be improved. Contractors consider cost factors as major factor affecting quality in the project whereas lack of supervision by contractors is cited by the clients as major factor affecting quality. To overcome the challenges of quality, proper material selection and usage, inspection, quality training, setting up quality department are seen as remedial measures. Improving safety at site, maintaining written correspondence, incorporating elements of ISO, proper selection and usage of materials, holding quality trainings and setting up quality control department are recommended for this project.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".