Use of Failure Case Studies in a Construction Planning and Estimating Course
Bibliographic record
Abstract
Reasons for including failure case studies within the undergraduate engineering curriculum have been made by various authors. Specific cases have been integrated into the syllabi of engineering mechanics, structural analysis, and structural design courses. The case studies used are typically catastrophic structural failures, where failure can be defined as a structural collapse, often with significant loss of life. Many civil engineering undergraduate programs include a course in construction planning and management. The American Society of Civil Engineers (ASCE) Civil Engineering Body of Knowledge (BOK) for the 21th Century, Second Edition emphasizes a need for Civil Engineers to understand not only the technical areas of analysis and design, but also more of the project management issues that enable a project to go from design to fruition. Hence, it is likely that more programs will include construction management courses. This paper deals with the integration of failure case studies into a construction planning and estimating course. In addition to structural failures, this course looks at the definition of "failure" from an engineering/construction perspective. It explains that failure includes the inability of an engineered facility to perform as designed. It further opines that failure to be constructed within reasonable time and budgetary constraints falls within the definition of failure. While most students may have the pre-conceived notion that failures are only caused by design deficiencies and perhaps natural hazards, the examples used in this course emphasize how many failures are caused by deficiencies in the design and/or construction process itself. In addition to the use of more typical and catastrophic failure case studies, such as the Hyatt Regency Walkway Collapse (Kansas City, Missouri), and the L'Ambiance Plaza Collapse (Bridgeport, Connecticut), the course makes extensive use of the Montreal Olympic Facility design and construction to demonstrate how deficiencies in the design and construction process can lead to extreme constructability issues and exorbitant cost overruns. The author also shows how the inclusion of the case studies addresses some of the more difficult areas to address in the Accreditation Board for Engineering and Technology (ABET) evaluation criteria, such as the understanding of the impact of civil and environmental engineering solutions in a global and social context.
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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 | 0.023 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".