Generative ai in the construction industry: Opportunities & challenges

P Ghimire, K Kim, M Acharya - arXiv preprint arXiv:2310.04427, 2023 - arxiv.org
In the last decade, despite rapid advancements in artificial intelligence (AI) transforming
many industry practices, construction largely lags in adoption. Recently, the emergence and …

Predicting mine water inrush accidents based on water level anomalies of borehole groups using long short-term memory and isolation forest

H Yin, Q Wu, S Yin, S Dong, Z Dai, MR Soltanian - Journal of hydrology, 2023 - Elsevier
Water level variation of explorational boreholes in mining sites is one of the most direct
representations of water inrush risk. Despite recent efforts on mine water inrush accident …

Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for …

E Uncuoglu, H Citakoglu, L Latifoglu, S Bayram… - Applied Soft …, 2022 - Elsevier
In this study, it was investigated that how machine learning (ML) methods show performance
in different problems having different characteristics. Six ML approaches including Artificial …

Opportunities and challenges of generative ai in construction industry: Focusing on adoption of text-based models

P Ghimire, K Kim, M Acharya - Buildings, 2024 - mdpi.com
In the last decade, despite rapid advancements in artificial intelligence (AI) transforming
many industry practices, construction largely lags in adoption. Recently, the emergence and …

Random forest algorithm and support vector machine for nondestructive assessment of mass moisture content of brick walls in historic buildings

A Hoła, S Czarnecki - Automation in Construction, 2023 - Elsevier
The article presents the results of experimental research and numerical analyses, and also
shows the usefulness of the random forest algorithm and the support vector machine for the …

A generative adversarial learning strategy for spatial inspection of compaction quality

J Li, X Wang, J Li, J Zhang, G Ma - Advanced Engineering Informatics, 2024 - Elsevier
Reliable prediction methods play a crucial role in enhancing the compaction quality and
implementing the intelligent compaction (IC). The predictive performance of contemporary …

Transfer-learning and texture features for recognition of the conditions of construction materials with small data sets

E Mengiste, KR Mannem, SA Prieto… - Journal of Computing …, 2024 - ascelibrary.org
Construction materials undergo appearance and textural changes during the construction
process. Accurate recognition of these changes is critical for effectively understanding the …

Using machine learning to improve cost and duration prediction accuracy in green building projects

A Darko, I Glushakova, EB Boateng… - Journal of Construction …, 2023 - ascelibrary.org
A major source of risk in green building projects (GBPs) is inaccurate human prediction of
the final project cost and duration, which in turn results in cost and schedule overruns (ie …

Process-oriented guidelines for systematic improvement of supervised learning research in construction engineering

V Asghari, MH Kazemi, M Shahrokhishahraki… - Advanced Engineering …, 2023 - Elsevier
A limited assessment of the development process and various stages of machine learning
(ML) based solutions for construction engineering (CE) problems are available in the …

Forecasting failure load of Sandstone under different Freezing-Thawing cycles using Gaussian process regression method and grey wolf optimization algorithm

D Fakhri, A Mahmoodzadeh, AH Mohammed… - Theoretical and Applied …, 2023 - Elsevier
The stability analysis of rock is an important basis to ensure the safe exploitation of
underground resources and the reliable operation of space engineering. Failure load is one …