Teaching Building Design and Construction Engineering. Are we ready for the paradigm shift?
Bibliographic record
Abstract
Drastic changes are occurring in the construction industry. Building Information Modeling (BIM) processes and technologies, and new Integrated Project Delivery (IPD) approaches are transforming the way buildings are planned, designed, built and operated. With the needs for new skills to cope with these accelerating changes, architecture, engineering and construction (AEC) associations in the United States are working with universities to reengineer teaching programs, integrating architecture training within an engineering and construction curriculum. Leading universities are already developing new programs, such as BIM studio courses, and promoting new ways to teach practice knowledge within design laboratories.These changes are also starting to occur in the Canadian industry. Some large governmental bodies are starting to request that their projects are designed and built using BIM. Canadian universities must respond to these changing requirements to prepare future architects, engineers, and construction managers for these new challenges and emerging industry needs. This paper provides examples for how to bridge this gap by bringing practice knowledge and research to the classroom. First, it synthesizes the impact of BIM and IPD on engineering practices in Canada. Second, it describes curriculum development undertaken between a school of architecture and two engineering departments for the development of multidisciplinary design studios to teach integrated design and BIM. Case studies are set in urban contexts and include the development of new buildings as well as refurbishment proposals for an industrial obsolete landmark. Finally, learning from this teaching and research experience, it raises questions and issues regarding our readiness to cope with this paradigm shift.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".