The Malaysian Construction Industry’s Risk Management in Design and Build
Bibliographic record
Abstract
Over the past twenty years, there have been large-scale and expensive public works tenders that adopted design and build procurement (D&B) contracts which consist of construction and civil engineering. Experts from the academic and construction circles have been studying the effective implementation of these tenders, the difficulties they encountered and the solutions for them. However, design and build (D&B) projects have additional stages such as the pre-planning and design and post-operative stages compared to traditional construction projects. As a result, contractors are faced with a higher chance of project risk probability and impacts. The risks that were to be assumed by the original employer may be transferred to the design and build contractors by means of a written agreement. Risk Management involves appropriate handling of risks after evaluation and analysis to minimize the negative impacts risks have on the finance with the lowest costs. Risk management is applied to establish necessary guidelines and to obtain key indices for successful design and build projects for contractor to follow. The method incorporated in this research included literature review, questionnaire surveys and interviews. The encountered risk factors were compiled and placed in the right sequence. The possible ways of minimizing risks that design and build members have on the project according to the definition and implementation of risk management been also looked into. From the findings, it is recommended that contractors should have clear employer briefing, clear specifications and statement of needs, good quality of workmanship, implementing code of practice, key elements effective management, effective communication with all components of the project team and decisive action in the event of deviation from plans for a successful (D&B) projects
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".