TY  - JOUR
VL  - 17
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183997530&doi=10.1016%2fj.dibe.2024.100349&partnerID=40&md5=3eef283124fb5c80512c211bf6691f20
A1  - Waqar, A.
A1  - Bheel, N.
A1  - Tayeh, B.A.
JF  - Developments in the Built Environment
SN  - 26661659
PB  - Elsevier Ltd
Y1  - 2024///
KW  - Concrete construction; Concretes; Construction industry; Image analysis; Pattern recognition; Quality control
KW  -  Artificial intelligence; Digital transformation; Image patterns; Image-analysis; Pattern recognition algorithms; Predictive maintenance; Quantitative research; Strength characteristics; The building industry
KW  -  Artificial intelligence
ID  - scholars19858
TI  - Modeling the effect of implementation of artificial intelligence powered image analysis and pattern recognition algorithms in concrete industry
N2  - AI-powered image analysis and pattern recognition algorithms (IAPRA) are renowned for their capacity to identify concrete flaws, assess strength characteristics, and anticipate the service life of concrete. However, its execution in a concrete building is challenging due to several unknown aspects. This research aims to evaluate the challenges encountered by AI-powered IAPRA and their influence on the concrete industry's digital transformation success. We conducted a quantitative research methodology to identify impediments and success variables associated with AI-powered picture analysis and pattern recognition algorithms. Structural Equation Modeling (SEM) tests were conducted to determine the critical hurdles associated with AI-powered picture analysis and pattern recognition algorithms. Three reliable and valid formative constructs were identified: complexity and privacy, economic and legal, and technology and integration. The developed model revealed the significance of three reflecting constructs: quality control, predictive maintenance, and enhanced productivity. The practical implications include, addressing the identified challenges related to AI-powered IAPRA is crucial for the concrete industry's digital transformation. By prioritizing quality control, predictive maintenance, and enhanced productivity, stakeholders can optimize concrete processes and outcomes. The major limitation of this study is its reliance on the quantitative research approach, which inherently restricts the data collection to the specific features of the sample under investigation. © 2024 The Author(s)
N1  - cited By 0
AV  - none
ER  -