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This dataset consists of the input files of the Ansys finite element models that were used to model the Riddes Viaduct. The four different scenarios considered are included (A, AR, B and BR).

Refereed/Peer-reviewed Mega construction projects have been a vital source of interest among different external stakeholders due to its complex nature. Although, the internal stakeholders are more concerned about a project, external stakeholders have a higher potential to influence mega construction projects by employing different strategies during the project life cycle. This has become a key issue for project owner since it leads to project suspension, delays, and cost overruns, which eventually leads to technical and internal project-related concerns. To achieve the research goal, a multiple case design was used, with two rounds of data-gathering methods comprising a desktop study and semi-structured interviews. Thirteen external stakeholder groups were identified. The outcomes of this research demonstrated that a diverse variety of external stakeholders arose among local governments, national governments, social and professional organisations, and political organisations are all common stakeholders in mega construction projects. Some stakeholders are particular to a project. Moreover, it is a significant fact that the project owner has the potential to successfully mitigate external influences without making many changes to the project scope. Thus, this article discussed external stakeholders’ impacts on mega construction projects throughout the project life cycle, as well as mitigation steps taken by project owners.

This study aims to provide an overview of the building condition assessment at the facility management stage in higher education buildings in Portugal. A questionnaire survey containing 188 questions was developed and was responded to by experts in the facility management departments of 19 higher education institutions (HEIs) in Portugal. The data analysis, using analytic hierarchy process, showed the difficulties related to the process of facility management and building condition assessment and the experts’ preferences in building condition assessment and facility management in Portuguese HEIs. Four building condition assessment parameters were also evaluated. Although not representative of all HEIs in Portugal, the findings indicate that there needs to be a significant change in the management of the HEI building stock with the support of new digital technologies and the prioritization of preventive actions. This unique view of facility management in Portugal provides useful pointers for facility managers working in HEI in other (European) countries and has relevance to other building typologies. published

School infrastructure affects the quality of education and the performance of children and youth. Natural hazards such as earthquakes, hurricanes, floods, and landslides, threaten critical infrastructure such as school facilities. Additionally, problems related to the functionality of these facilities are common in the region, such as an inadequate number of classrooms, poor lighting, and insufficient ventilation, among others. At a national level, the decision-making process to prioritize schools’ interventions becomes even more challenging due to limited resources and lack of information. Furthermore, there is a lack of a systematic approach to address the need of improving existing infrastructure taking into consideration limited resources. Considering this, a novel decision-making framework is proposed that prioritizes school infrastructure investment with limited budgets, using clustering procedures, a multi-criteria utility function, and an optimization component. This framework allows better public policy decisions and benefits students in terms of buildings quality with a multi-criteria perspective, improving both safety and functional conditions. The framework is illustrated with a case study applied to the public-school infrastructure in the Dominican Republic.

open access article This work is based on the PhD project of Ling Tian. Decentralization and a lack of integration in the architecture, engineering and construction (AEC)industry are some of the main challenges for project management in green building construction.To address this problem, many construction companies have attempted to use buildinginformation modelling (BIM) to coordinate and optimize the management of green buildingprojects. However, the research on successfully adopting and using BIM in interdisciplinaryteams working on green building projects is limited; moreover, comprehensive comparisonsacross different countries and regions are lacking. Therefore, this study conducts a novelinvestigation of the key factors affecting the use of BIM in project management for greenbuilding construction through case studies in the UK and China. This study uses semi-structuredinterviews, Python-based term frequency analysis and thematic analysis to identifyfive keythemes, namely, communication, data environment, motivation, project members and policy,which influence the adoption and implementation of BIM. In addition, based on gained insights,the impacts of different green building certification levels on BIM project management duringthe construction phase must be fully considered. Thesefindings provide practicalrecommendations for BIM management in green building construction projects and contributeto thefield of construction management.

Integrating 3D point cloud data in BIM can enhance the understanding and visualization of the actual state of the interior construction. We propose an overall process of registering as-is point cloud data to a BIM model and integrating as-is information to generate a 4D as-is BIM. The point cloud data of a portion of a complex multi-building construction is automatically registered in the planned BIM using an automated room floor plan search and matching algorithm. The as-is information obtained from the semantic features of the point cloud is integrated with the planned BIM. The BIM resulting from the integration is enriched with information about the construction status of elements at the specific time of the data collection. The proposed method was demonstrated on a real construction site, where point cloud data was collected four times, capturing the construction progress over about a month. The search and matching algorithm accurately matched all the sample rooms on the planned BIM model with the room shapes obtained from respective as-is point clouds. Furthermore, the as-is information was effectively extracted from the raw point cloud data instances and automatically integrated into the as-planned BIM.