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This example deals with managing the workability of concrete for a raft foundation project under hot weather conditions. Workability is crucial for ensuring the concrete is easy to place, compact, and finish. In this scenario, a slump test — a common method to measure the workability of fresh concrete — initially indicated low workability. This was attributed to the rapid water loss from the concrete mix, exacerbated by the high temperatures causing the course aggregates to heat up.
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Characteristics-Based Framework of Effective Automated Monitoring Parameters in Construction Projects.

Abdul Hannan Qureshi1, Wesam Salah Alaloul1, Wong Kai Wing1

  • 1Department of Civil and Environmental Engineering, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan Malaysia.

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Summary
This summary is machine-generated.

Digitalization in construction faces adoption hurdles due to a lack of theoretical understanding. This study introduces a conceptual framework with 21 parameters for effective automated construction progress monitoring, enhancing user comprehension.

Keywords:
Automated monitoring frameworkConfirmatory factor analysisDetection technologiesEffective monitoring factorsExploratory factor analysis

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Area of Science:

  • Construction Management
  • Digitalization in Construction
  • Automated Monitoring Technologies

Background:

  • The construction industry is increasingly adopting digital technologies for various processes.
  • Automated construction progress monitoring techniques are emerging, but adoption is slow.
  • A lack of theoretical understanding regarding effective implementation hinders the uptake of these technologies.

Purpose of the Study:

  • To analyze technological parameters relevant to automated monitoring in construction.
  • To develop a theoretical, conceptual framework explaining factors influencing automated monitoring operations.
  • To provide practitioners with a foundational understanding of technological parameters affecting digital monitoring outcomes.

Main Methods:

  • A systematic process including literature review, interviews, pilot and questionnaire surveys.
  • Application of Structural Equation Modeling (SEM)-based mathematical modeling.
  • Identification and categorization of 21 key parameters influencing automated monitoring.

Main Results:

  • A refined conceptual framework with 21 effective parameters across five categories: Target Object, Technical, External Interference, Occlusions, and Sensing.
  • Establishment of a knowledge framework using SEM, based on characteristic themes.
  • The framework offers a theoretical basis for understanding technological parameters in automated monitoring.

Conclusions:

  • The developed conceptual framework provides a theoretical foundation for practitioners in automated construction monitoring.
  • It addresses the need for a clearer understanding of technological parameters impacting digital monitoring performance.
  • This study offers a unique, user-friendly overview of general criteria affecting digital monitoring outcomes.