Archaeological sites have been subjected to preservation efforts due to their significant historical and cultural value, as well as their vulnerability. Several advanced digital imageries and modeling technologies have been proposed in the literature and adopted in practice to obtain accurate data required to manage and restore archaeological sites. This study proposes an integrated data collection and analysis methodology combining aerial and close-range photogrammetry, geographic information systems (GIS), and ground-penetrating radar (GPR) technologies to capture essential geospatial and geophysical information for preserving archaeological sites. The integrated methodology was applied and demonstrated with data and information collected from the important archaeological site of Qaser Amra, which is an ancient castle located in the desert in Jordan. The proposed methodology generated various levels of details, including a 2.5-dimensional geo-reference model, a GIS vector site layout, and a three-dimensional (3D) textured model. Subsurface detection of anomalies was performed across the site using the GPR technology. Most anomalies indicated shallow subsurface walls buried within depths ranging from half to one meter and at different extensions. Moreover, based on the GPR data, the horizontal and vertical extent of Qaser Amra’s walls were defined using 3D imaging. The integrated 3D archaeological model of Qaser Amra can be used for archaeological documentation, maintenance and monitoring, conservation, tourism, and urban planning.

Purpose: This research aims to identify, rank and evaluate the impact of delay factors on the timely completion of the handover stage of construction projects in the United Arab Emirates (UAE). Design/methodology/approach: The study was conducted in two stages. Initially, an extensive literature review and focus groups with industry professionals were utilized to identify and rank delay factors specific to the UAE’s construction sector. Second, data were collected from multiple construction firms in the UAE, and a delay estimation model was developed and subsequently validated. Findings: A total of 30 delay factors were identified and ranked according to their significance index values. The top five factors were linked to various stakeholders, including subcontractors, general contractors, customers and project owners. The developed model demonstrated an accuracy of 71% on the test dataset. Research limitations/implications: While the model shows promising results, its accuracy could be further enhanced with additional data or through the exploration of more advanced nonlinear predictive models. Practical implications: The findings can assist various construction project stakeholders to better manage and mitigate delays, potentially leading to more successful project completions. Social implications: Improving the efficiency of the handover process can lead to faster project completions, which in turn can reduce the social inconvenience caused by prolonged construction activities. Originality/value: The study is novel in focusing specifically on the UAE and Gulf region, where previous research is limited. The development and validation of a delay estimation model tailored to this regional context enhances the originality of the research.