A data-driven framework for conceptual cost estimation of infrastructure projects using XGBoost and Bayesian optimization-Reference-Cited by-同舟云学术

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A data-driven framework for conceptual cost estimation of infrastructure projects using XGBoost and Bayesian optimization

Published:2024-01-04 Issue: Volume: Page:1-24
ISSN:1346-7581
Container-title:Journal of Asian Architecture and Building Engineering
language:en
Short-container-title:Journal of Asian Architecture and Building Engineering

Author:

Zhang Jiashu¹,Yuan Jingfeng¹,Mahmoudi Amin¹,Ji Wenying²,Fang Qiushi³

Affiliation:

1. Department of Construction and Real Estate, School of Civil Engineering, Southeast University, Nanjing, China

2. Department of Civil, Environmental and Infrastructure Engineering, George Mason University, Fairfax, VA, USA

3. Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, UK

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/13467581.2023.2294871

Reference57 articles.

1. Performance evaluation of normalization-based CBR models for improving construction cost estimation

2. Design information extraction from construction specifications to support cost estimation

3. Machine learning-based models for predicting the shear strength of synthetic fiber reinforced concrete beams without stirrups

4. Evaluating the Impact of External Support on Green Building Construction Cost: A Hybrid Mathematical and Machine Learning Prediction Approach

5. Explaining anomalies detected by autoencoders using Shapley Additive Explanations

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