The Architecture, Engineering, Construction and Operations (AECO) industry is generally slow in adopting emerging technologies, and such hesitance invariably restricts performance improvements. A plethora of studies have focused on the barriers, Critical Success Factors (CSFs), lifecycle and drivers independently, but none have explored the impact of BIM drivers and awareness on the project lifecycle. This study empirically explored the impact of BIM drivers and awareness on the project lifecycle using Structural Equation Modelling (SEM). Initially, a conceptual model was developed from an extensive literature review. Thereafter, the model was tested using primary questionnaire data obtained from 90 construction professionals in Lagos, Nigeria. Emergent findings indicate that Building Information Modelling (BIM) drivers have a high impact on BIM awareness at the operation stage of the project lifecycle. The SEM model has an average R² value of 23% which is moderate. Consequently, this research contributes to the existing body of knowledge by providing invaluable insight into the impact of BIM drivers on BIM awareness in the project lifecycle. Knowledge acquired will help industry stakeholders and government to develop appropriate policies to increase BIM uptake within contemporary practice.

Cloud Computing has become a valuable platform for sustainability in many countries. This study evaluates the cloud computing implementation and its Critical Success Factors (CSFs) towards ensuring sustainable construction projects in Nigeria. Data were collected from previous literature, supplemented by a quantitative approach via a questionnaire survey. Data were collected from 104 construction professionals while cloud computing CSFs were examined using Relative Importance Ranking (RII) and Exploratory Factor Analysis (EFA). The results show that cloud computing’s awareness level is 96.2%, which means that the respondents are aware of cloud computing concept. Furthermore, the result shows that most of the respondents are adopting the concept. The analysis of the CSFs indicated that reliable data storage, performance as well as cost of accessibility and availability were the four most significant CSFs to cloud computing applications. Analysis of the CSFs through EFA generated four main components which include human satisfaction, organization, client’s acceptance, and industry-based. Consequently, this study contributed to existing body of knowledge by highlighting the cloud computing CSFs for achieving sustainable construction project. As such, the results could be a game-changer in the construction industry—not only in Nigeria but also in developing nations where construction projects are implemented through similar style and procedure. This study would be a benchmark for supporting decision-makers to improve data fragmentation, in which the use of data is paramount to the execution of construction works. Finally, the results of this study would be useful for enhancing sustainability and general management of construction projects through cloud computing implementation.

Accidents are more prevalent in the construction industry compared to other economic sectors. Therefore, understanding the benefits of occupational health and safety management systems (OHSMSs) in terms of their sustainable implementation, management and performance, as well as the awareness of OHMSs and barriers to their implementation, are important for improving OHSMSs in the sustainability of the construction industry. Although there is considerable research on OHSMSs, further assessments are needed concerning other aspects of OHSMSs, particularly the benefits of OHSMSs. Thus, this review paper summarises the empirical state of the art of OHSMS activities. Scopus, Web of Science and other databases were searched using predefined standards. The query was limited to articles published from 1999 to 2023. Consequently, one hundred and four articles were selected and analysed. These articles present analyses of OHSMSs and their potential benefits concerning the implementation of OHSMSs and management, performance, awareness, and barriers in relation to OHSMSs. The results reveal that 12.50% of the reviewed studies assessed the implementation of OHSMSs in the construction industry, and 25.96% studied the management of OHSMSs. Analyses of the performance of OHSMSs in the construction industry accounted for 8.65%, analyses of the awareness of OHSMSs accounted for 4.81%, model-related analyses accounted for 13.46%, studies on the significance/benefits of OHSMSs accounted for 3.85%, studies on the barriers/challenges associated with OHSMSs accounted for 5.77%, analyses on the safety indicators of OHSMSs accounted for 2.88% and other types of studies accounted for 20.19%. This study further reveals that the implementation of OHSMSs is characterised by a dearth of proper communication, the non-utilisation of personal protective equipment (PPE), wrong postures and work activities, a dearth of training, physiological factors including burnout and stress, and a dearth of safety culture and orientation; in addition, matters relating to compliance with effective laws are significant safety challenges in the construction industry. However, the rationality for evaluating the benefits of OHSMSs, comprising their implementation, management and performance, as well as awareness of and barriers to OHSMSs, is challenging to authenticate because appropriate field, survey, organisational and clinical data concerning incident occurrences in the construction industry are lacking for comprehensive evaluations. Thus, this novel study presents our effort to narrow this gap by establishing a framework for increasing our understanding of the benefits of implementing OHSMSs and accident reduction.

Value management (VM) has become a useful tool for achieving sustainability in many countries. This paper aims to assess VM implementation and its activities towards achieving sustainable building projects in Egypt. Data were obtained from the literature, followed by a qualitative approach through a semi-structured interview and a quantitative approach via a questionnaire survey. In Cairo and Giza, data were collected from a sample of 200 building professionals using a questionnaire, while exploration of the country’s VM activities practice was completed using exploratory factor analysis (EFA), including descriptive statistics such as “frequency analysis” and ”measures of central tendencies”. The results show that VM awareness was demonstrated by 64% of the sample, which means that they know about VM. On the other hand, most respondents, 85.3%, did not adopt VM and did not receive any VM training. The results also show, through EFA, that the correlations between these activities show five main components: an information phase, a function phase, a creativity phase, an evaluation phase, and a development/presentation phase. This study will help building professionals to eliminate unwanted costs and enhance project sustainability by adopting VM in building projects in developing countries. Finally, the results of this study will enhance building management through the implementation of VM elements, with a view to ensuring value for money and meeting sustainability goals.

The construction industry generates a substantial volume of solid waste, often destinated for landfills, causing significant environmental pollution. Waste recycling is decisive in managing waste yet challenging due to labor-intensive sorting processes and the diverse forms of waste. Deep learning (DL) models have made remarkable strides in automating domestic waste recognition and sorting. However, the application of DL models to recognize the waste derived from construction, renovation, and demolition (CRD) activities remains limited due to the context-specific studies conducted in previous research. This paper aims to realistically capture the complexity of waste streams in the CRD context. The study encompasses collecting and annotating CRD waste images in real-world, uncontrolled environments. It then evaluates the performance of state-of-the-art DL models for automatically recognizing CRD waste in-the-wild. Several pre-trained networks are utilized to perform effectual feature extraction and transfer learning during DL model training. The results demonstrated that DL models, whether integrated with larger or lightweight backbone networks can recognize the composition of CRD waste streams in-the-wild which is useful for automated waste sorting. The outcome of the study emphasized the applicability of DL models in recognizing and sorting solid waste across various industrial domains, thereby contributing to resource recovery and encouraging environmental management efforts.