研究

概要

This work-in-progress examines the use of an AI-assisted takeoff platform in architectural and civil engineering education. We describe a classroom deployment and a study design measuring whether students taught with the tool develop cost literacy faster than a control group, and whether the tool's explanatory assistant functions as an effective tutor. Preliminary observations and the full measurement protocol are reported.

概要

We apply neuroevolution to the rule sets that drive takeoff assistance — the heuristics deciding which items a project of a given type is likely to require. Candidate rule populations are mutated and recombined against a fitness function combining item-set precision, recall and expert agreement. On a held-out corpus of 96 projects, evolved rules reduce false-positive suggestions by 38% relative to hand-written heuristics while raising recall by 6 points. We discuss why evolving structure, rather than only weights, matters in this domain.

概要

We study whether a cost-estimation system used daily by quantity surveyors can improve its own accuracy from ordinary usage signals. Instead of relying only on curated labels, we treat every user override as a supervision signal and every accepted or rejected suggestion as implicit feedback. Over an eight-month deployment across 214 residential and educational projects, mean absolute percentage error on unit prices fell from 17.4% to 9.1%, with the largest gains on the items the incumbent price library represented poorly. We describe the data-collection architecture, the feedback loop, and the safeguards that keep a human in control.

概要

Price suggestions are only useful if surveyors can justify them. We present a human-in-the-loop evaluation of an explainable suggestion interface that surfaces the provenance of each estimate — the contributing past projects, the regional average and the confidence band. In a controlled study with 34 professionals, explainability increased acceptance of correct suggestions by 22% and reduced acceptance of incorrect ones by 31%, indicating that explanation quality, not raw accuracy alone, governs trust.