Automatic Price Prediction from CAD Files

Journal
Proceedings of the 18th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
ISSN
2184-3228
ISSN-Digital
2184-3228
ISBN
978-989-758-868-6
Type
conference paper
Date Issued
2026-10-28
Author(s)
Sutter, Robin
;
Holenstein, Yannik
;
;
DOI
Abstract
The paper presents a proof of concept to automate quotation generation for modular construction, thereby replacing a slow, manual process. From 975 priced STEP models, about 40 geometric and structural features were extracted to make the data suitable for machine learning. Several models were tested: while LSTM on raw STEP data failed, tree-based models (random forest and especially XGBoost) performed best, with XGBoost using six key features. The deep-learning-based TabNet underperformed due to the limited dataset size. Due to the limited number of samples for expensive designs, a two-stage pipeline is proposed: a model handles typical cases, while a classifier flags high-priced cases for manual quotation.
Language
English (United States)
Keywords
CAD-Based Price Prediction
Feature Engineering
Gradient Boosting
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
SCITEPRESS - Science and Technology Publications
Publisher place
Portugal
Volume
1
Start page
217
End page
224
Pages
8
Event Title
18th International Conference on Knowledge Discovery and Information Retrieval
Event Location
Angers, France
Event Date
28-30 October 2026
Official URL
URL
Subject(s)

computer science

Division(s)

ICV - Institute for C...

Contact Email Address
bella.fernandez@unisg.ch
File(s)
Thumbnail Image

restricted

Name

152969.pdf

Type

Main Article

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1.2 MB

Format

Adobe PDF

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open.access

Name

KDIR_Automatic price prediction from CAD files.pdf

Description
Poster shown during the conference
Size

1.03 MB

Format

Adobe PDF

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