Search results

Rapid urbanization and the shortcomings of traditional construction methods motivate construction professionals to explore faster and more sustainable approaches such as off-site construction (OSC). Thus, the purpose of this paper is to identify the drivers influencing OSC adoption and to explore the key drivers of its widespread adoption in Egypt.

A comprehensive global literature review was performed initially to develop an up-to-date list of OSC adoption drivers, which was confirmed in the Egyptian context through a pilot study. Then, social network analysis (SNA) was utilized to determine the most influential drivers as well as shortlist them to construct the final questionnaire survey. A total of 57 stakeholders in the Egyptian construction industry responded. Lastly, the relative importance index (RII) was calculated to rank the drivers, revealing the key drivers.

The results revealed that higher productivity, improving project quality control, shortening construction time, improving product quality and improving supervision and inspection are the top five drivers. On the contrary, government policies and regulations is the least significant driver.

This research contributes to the body of knowledge by introducing a comprehensive, up-to-date list of drivers, which helps the stakeholders gain a better understanding of the driving enablers of adopting OSC generally and helps Egyptian stakeholders make more informed decisions about its implementation specifically.

In today’s intricate and dynamic construction sector, traditional project management techniques, which view projects in isolation, are no longer sufficient. Project Portfolio Management (PPM) has proven to be an efficient alternative solution for handling multiple construction projects. As such, based on a Machine Learning (ML) approach, this study aims to explore the Critical Success Factors (CSFs) influencing the adoption of PPM, aiming to enhance PPM implementation in construction projects.

A questionnaire based on CSFs gathered from prior studies was developed and validated by experts. Afterward, exploratory data analysis is conducted to understand CSF–PPM relationships. Preprocessing techniques ensure uniformity in variable magnitudes. Lastly, ML techniques, namely Linear Discriminant Analysis (LDA), Logistic Regression (LR) and Extra Trees Classifier (ETC) are developed to model and investigate CSFs' impact on PPM adoption.

The findings pointed out that the ETC model marginally outperforms other ML models with a classification accuracy of 93%. Also, the project size, utilized PPM tool and resource allocation-related factors are the most significant CSFs that influenced the PPM performance by about 48.5%.

This study contributes to the existing body of knowledge by raising awareness among construction companies and other project stakeholders about the pivotal CSFs that must be considered when prioritizing projects and designing an optimal PPM approach.