The construction industry is one of the largest contributors to the world economy. However, the level of automation and digitalization in the construction industry is still at its infancy in comparison with other industries due to the complex nature and the large size of construction projects. Meanwhile, construction projects are prone to cost overruns and schedule delays due to the adoption of traditional progress monitoring techniques to retrieve progress on-site, having indoor activities participating with an accountable ratio of these works. Improvements in deep learning and Computer Vision (CV) algorithms provide promising results in detecting objects in real time. Also, researchers have investigated the probability of using CV as a tool to create a Digital Twin (DT) for construction sites. This paper proposes a model utilizing the state-of-the-art YOLOv8 algorithm to monitor the progress of bricklaying activities, automatically extracting and analyzing real-time data from construction sites. The detected data is then integrated into a 3D Building Information Model (BIM), which serves as a DT, allowing project managers to visualize, track, and compare the actual progress of bricklaying with the planned schedule. By incorporating this technology, the model aims to enhance accuracy in progress monitoring, reduce human error, and enable real-time updates to project timelines, contributing to more efficient project management and timely completion.

Prefabrication construction (PC) is a greener and more sustainable building method that must be used to achieve the project's sustainability. PC is a construction method in which modules are manufactured in a factory and then placed on-site. Although PC is widely used in developed nations, its applications in developing countries remain extremely limited. The primary objective of this investigation is to analyze and introduce a model for the fundamental factors that impede the implementation of prefabrication in the construction sector of Egypt. In order to attain the desired objective, a comprehensive literature review, expert consultations, and face-to-face interviews were conducted to gather 23 observable variables that significantly influence the prefabrication construction industry. A questionnaire form was developed and distributed among professionals in the construction field, and a total of 371 replies were gathered during the collection of data. Following that, fuzzy theory was utilized for analyzing the probability and impact data from the questionnaire survey to determine the risk relevance for each factor. Then, a model consisting of 15 variables and five major latent dimensions was produced. A proposed SEM model was developed in the next phase using the measurement and the structural model. The findings indicate that the policy factor and attitudinal factors have a predominant influence. The current study provides meaningful data about the aspects covered, which can help in the development of effective strategies for promoting prefabricated construction.