Evidence map›Paper›PMID 41888534›Full record

ArticleScientific reports2026

Uncertainty aware and explainable construction cost prediction using a hybrid probabilistic learning model.

Lifei Chen, Othman Waleed Khalid, Jun-Jiat Tiang, Sew Sun Tiang, Dong-Youn Shin, Yit Hong Choo, Abhishek Sharma, Wei Hong Lim

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Lifei ChenFaculty of Engineering, Technology and Built Environment, UCSI University, 56000, Kuala Lumpur, Malaysia.
Othman Waleed KhalidSchool of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300, Nibong Tebal, Penang, Malaysia.
Jun-Jiat TiangCentre for Wireless Technology, CoE for Intelligent Network, Faculty of Artificial Intelligence & Engineering, Multimedia University, Persiaran Multimedia, 63100, Cyberjaya, Selangor, Malaysia. jjtiang@mmu.edu.my.
Sew Sun TiangFaculty of Engineering, Technology and Built Environment, UCSI University, 56000, Kuala Lumpur, Malaysia.
Dong-Youn ShinFaculty of Engineering, Technology and Built Environment, UCSI University, 56000, Kuala Lumpur, Malaysia.
Yit Hong ChooSwinburne Research Office, Swinburne University of Technology, Hawthorn, VIC, 3122, Australia.
Abhishek SharmaDepartment of Computer Science and Engineering, Graphic Era (Deemed to Be) University, Dehradun, 248002, India.
Wei Hong LimFaculty of Engineering, Technology and Built Environment, UCSI University, 56000, Kuala Lumpur, Malaysia. limwh@ucsiuniversity.edu.my.

Funding

Malaysian Ministry of Higher Education FRGS/1/2024/ICT02/UCSI/02/1
6 · The paper itself

Abstract

This paper presents a unified probabilistic framework for construction cost forecasting, NGBoost-ETR (Natural Gradient Boosting with Extra Trees base learners) that delivers predictive accuracy, calibrated uncertainty, and SHAP-based interpretability. Rather than positioning novelty in algorithmic integration alone, the contribution lies in a systems-level design that jointly addresses three critical gaps in cost modeling: reliable interval calibration, model interpretability, and robustness across complex feature interactions in domain such as sustainable building. Trained on a real-world RSMeans dataset of 4477 samples, NGBoost-ETR achieves superior predictive performance (R

Indexed as

Construction cost predictionExplainable artificial intelligence (XAI)Hybrid machine learningNatural gradient boostingUncertainty quantification

Identifiers

PMID41888534
PMCPMC13039491

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.