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QUALITY MANAGEMENT PRACTICES IN GONDRANG-PULCHOWK SIX-LANE ROAD UPGRADING PROJECT AT CHITWAN DISTRICT, NEPAL

2023· article· en· W4380442580 on OpenAlexaff
Aashika Pokharel, Bipana Lamichhane

Bibliographic record

VenueInternational Journal of Research -GRANTHAALAYAH · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsCentennial College
Fundersnot available
KeywordsQuality (philosophy)Quality assuranceQuality managementRanking (information retrieval)Operations managementRemedial educationTransport engineeringBusinessComputer scienceEngineeringManagement systemMathematics

Abstract

fetched live from OpenAlex

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.

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)0.020
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.391
GPT teacher head0.578
Teacher spread0.187 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

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