A hybrid Bayesian optimization-based deep learning model for modeling the condition of saltwater pipes in Hong Kong-Reference-Cited by-同舟云学术

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A hybrid Bayesian optimization-based deep learning model for modeling the condition of saltwater pipes in Hong Kong

Published:2024-02-05 Issue: Volume: Page:1-17
ISSN:1562-3599
Container-title:International Journal of Construction Management
language:en
Short-container-title:International Journal of Construction Management

Author:

Mohammed Abdelkader Eslam¹²,Zayed Tarek¹,Elshaboury Nehal¹³,Taiwo Ridwan¹

Affiliation:

1. Department of Building and Real Estate, Faculty of Construction and Environment, Hong Kong Polytechnic University, Kowloon, Hong Kong

2. Structural Engineering Department, Faculty of Engineering, Cairo University, Giza, Egypt

3. Construction and Project Management Research Institute, Housing and Building National Research Centre, Giza, Egypt

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15623599.2024.2304392

Reference66 articles.

1. Optimizing Machine Learning Algorithms for Landslide Susceptibility Mapping along the Karakoram Highway, Gilgit Baltistan, Pakistan: A Comparative Study of Baseline, Bayesian, and Metaheuristic Hyperparameter Optimization Techniques

2. Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms

3. Failure modeling of water distribution pipelines using meta-learning algorithms

4. How Cost Effective Is Machine Learning/AI Applied to Leak Detection and Pipe Replacement Prioritization?

5. Electricity production based forecasting of greenhouse gas emissions in Turkey with deep learning, support vector machine and artificial neural network algorithms

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