Machine learning-based method for predicting compressive strength of concrete

D Li, Z Tang, Q Kang, X Zhang, Y Li - Processes, 2023 - mdpi.com
Accurate prediction of the compressive strength of concrete is of great significance to
construction quality and progress. In order to understand the current research status in the …

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

G Almasabha, KF Al-Shboul, A Shehadeh, O Alshboul - Structures, 2023 - Elsevier
Stirrups can be added to reinforced concrete (RC) beams made of plain concrete to increase
the shear strength providing better performance against embrittlement. If the need for shear …

Smart and automated infrastructure management: A deep learning approach for crack detection in bridge images

H Inam, NU Islam, MU Akram, F Ullah - Sustainability, 2023 - mdpi.com
Artificial Intelligence (AI) and allied disruptive technologies have revolutionized the scientific
world. However, civil engineering, in general, and infrastructure management, in particular …

Evaluating the impact of external support on green building construction cost: A hybrid mathematical and machine learning prediction approach

O Alshboul, A Shehadeh, G Almasabha, REA Mamlook… - Buildings, 2022 - mdpi.com
As a fundamental feature of green building cost forecasting, external support is crucial.
However, minimal research efforts have been directed to developing practical models for …

Multi-objective optimization of concrete mix design based on machine learning

W Zheng, Z Shui, Z Xu, X Gao, S Zhang - Journal of Building Engineering, 2023 - Elsevier
This study proposes a multi-objective optimization (MOO) framework for optimizing concrete
mixture proportions. Advanced methods such as K-fold cross-validation, Bayesian …

[HTML][HTML] An AI-driven model for predicting and optimizing energy-efficient building envelopes

LD Long - Alexandria Engineering Journal, 2023 - Elsevier
Unlike many previous studies that often focus on optimizing energy efficiency for buildings
when detailed design drawings are available, this paper introduces a newly integrated …

A comparative study of shear strength prediction models for SFRC deep beams without stirrups using Machine learning algorithms

O Alshboul, G Almasabha, KF Al-Shboul, A Shehadeh - Structures, 2023 - Elsevier
This study aims to evaluate the shear strength of stirrups-free Steel Fiber Reinforced
Concrete (SFRC) deep beams and to predict their shear strength values using Machine …

Exploring the efficacy of machine learning models for predicting soil radon exhalation rates

KF Al-Shboul, G Almasabha, A Shehadeh… - … Research and Risk …, 2023 - Springer
The correlation between soil radon exhalation rate (Rn-ER) and its natural radionuclide
content is complex, making quantification difficult using traditional regression methods. This …

A Relevance-Based Technology–Organisation–Environment Model of Critical Success Factors for Digital Procurement Adoption in Chinese Construction Companies

G Luo, C Serrao, D Liang, Y Zhou - Sustainability, 2023 - mdpi.com
With the emergence of digital transformation, there is an increasing need for Chinese
construction companies to adopt digital procurement (D-procurement). However, there is a …

The relationship between economic complexity and green economy with earnings management

Z Ahmadi, M Salehi, M Rahmani - Journal of Facilities Management, 2023 - emerald.com
Purpose This study aims to analyze the relationship between economic complexity (EC) and
the green economy (GE) with the real and accrual earnings management (REM and AEM) of …