Aisha Rahman, PEng

Structural Engineer and PEng with 16 years designing and verifying load-bearing systems across Canada's most demanding construction environments.

Aisha Rahman is a licensed Professional Engineer based in Toronto, Ontario, with sixteen years of practice spanning high-rise residential towers, industrial facilities, and mid-rise mass timber construction. At Entuitive Corporation, she leads structural assessments for mixed-use developments across the Greater Toronto Area and advises project teams on code-compliant load path design under the National Building Code of Canada. Her early career at Hatch Ltd. exposed her to heavy civil and resource-sector infrastructure, where she developed rigorous habits around load tabulation, concrete mix optimization, and cold-climate construction constraints that continue to shape her technical work.

Rahman holds graduate training in structural engineering from the University of Toronto, where her thesis examined the flexural behaviour of high-performance concrete slabs under eccentric loading. That research background translates directly into the calculator tools she authors and reviews: she treats each formula as a claim that must be traceable to a published standard, CSA A23.3, CSA O86, or equivalent international codes, and she stress-tests edge cases against values she has verified by hand on real project drawings. Her work on construction estimating tools draws on years of preparing quantity takeoffs and material schedules for Class B and Class C estimates on projects ranging from parking structures to long-span roof systems.

On OnlyCalculators, Rahman maintains the suite of structural and construction tools covering concrete volume and reinforcing, lumber sizing, tributary area calculations, and dead and live load accumulation. Practitioners from site supervisors to licensed engineers use these tools as quick checks before consulting full design software, and her review process reflects that dual audience: inputs must be clearly defined, output units unambiguous, and underlying assumptions stated explicitly so users understand where the simplified model applies and where it does not.