Data analytics and big data in construction project and asset management

Data analytics and big data in construction project and asset management The increasing adoption of digital technology and the rapid proliferation of data have spurred the application of data analytics and big data to drive smart project and asset management.We are likely to see the rise of new approaches to information management and data usage within the architecture, engineering and construction (AEC) sector.Transforming data and information into knowledge and intelligence would change the way projects and assets are managed and will facilitate optimal solutions across the sector.This special journal issue provides a forum to explore, develop and disseminate emerging concepts of data analytics and big data and their potential applications and opportunities in the AEC.In the opening paper of this special issue, Marzouk and Enaba explores how data in building information model (BIM) can be furnished with descriptive analytics within the BIM environment to analyse construction project performance.They developed a dynamic model that helps in detecting hidden patterns and different progress attributes from construction project raw data.Their study assumes that integrating BIM and data analytics in a construction project is beneficial.They then validated this assumption on a case study project.Marzouk and Enaba study is interesting because by leveraging data embedded in BIM to gain actionable insight using analytics, the AEC sector can increase the benefit and value derived from BIM efforts on projects thereby increasing the adoption of BIM and big data.The second paper by Farghaly, Abanda, Vidalakis and Wood investigates "the transfer of information from the BIM models to either conventional or advanced asset management platforms using Linked Data".Employing a participatory action research approach with focus group and interviews as well as prototyping, they proposed a process for generating linked data in the asset management context and its integration with BIM data.In view of the very limited application of BIM data for asset management, the process proposed in their paper can improve the data exchange from BIMs to asset management systems during handover stage and consequently improve asset management outcomes during the operation and maintenance stage.Applying the process that they have developed to a real-world case study of BIM for facilities management would provide empirical evidence of the value and challenges of BIM-linked data integration for asset management.Amit Mitra and Kamran Munir focus on how big data can influence asset management.Drawing from interviews of leaders of digital transformation projects in three organisations that are within the insurance industry, natural gas and oil, and manufacturing industries, they argued that as asset information becomes a project deliverable, and as data increases in volume, velocity and variety; and as it is aggregated and re-used, there is need to improve semantic processors to deal with the vast array of data in variable formats.Evidently, the increasing volume and variety of asset management data will make the implementation data analytics inevitable if organisation must derive value from their asset information.Although the research is still in its infancy, it is interesting because it reminds us that the value of asset information does not lie in the volume of the data itself but in the insight that organisations can garner from the data to achieve better asset management outcomes.Still on the application of big data and analytics, Jafari and Akhavian use data analytics models to predict the driving forces of housing prices arguing that this can be extremely useful in the built environment and real estate decision-making processes.Based on a data set of 13,771 houses, they developed a hedonic pricing method of the key factors that affect the residential housing prices across the USA.They tested and validated their model using 22 houses not used in models.The results of their study show that the main driving