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This study was conducted to identify, summarise, analyse and categorise mobile device applications (Apps), relevant to the construction industry and to explore their uses and exposure levels.

The research method involved reviewing literature and searching for Apps. The construction Apps were found by developing key phrases. These key phrases were used to develop search strategies, which were then used to find the construction Apps. The Apps found were categorised based on the similarity of their uses.

The 136 Apps identified were summarised, analysed, and categorised into 11 groups of distinct construction operations and tasks. The “Design and Drawing Apps”, “Measurement and Estimation Apps”, “Management Apps”, “All Round Apps” and “Construction Site Apps” recorded 29, 28, 26, 21 and 11 numbers of Apps, respectively. The Autodesk Sketchbook, GPS Field Area Measure, MagicPlan, Measure and TSheets were the top five in terms of the number of downloads. These Apps in terms of their exposure levels in the construction industry record 4.76%, 2.38%, 0.52%, 0.48% and 0.42%, respectively.

This paper provides a catalogue of the continuum of construction Apps for a wide variety of construction operations/activities which are available for construction professionals and provide guidance on their uses to assist in selecting appropriate Apps for specified operation/tasks/activities in the construction industry. Construction professionals may benefit from increased productivity, efficiency and ease of working.

The concept of “regenerative business” is thriving in current business literature. The present study seeks to contribute to the current academic debate by investigating the nature and scope of regenerative hospitality, here seen as a steppingstone of regenerative tourism.

Exploratory in nature and with the goal of understating the nature and scope of regenerative hospitality, nineteen semi-structured interviews with academics, consultants and self-proclaimed regenerative hoteliers were conducted.

Results provide a regenerative hospitality framework to move from the current sustainability paradigm towards local and systemic regenerative approaches in hospitality by applying place and people intelligence.

This research contributes to the current academic debate about the future of travel, particularly focussing on the future of hospitality in relation to the multidisciplinary field of regenerative economy. Particularly, the paper has been designed to contribute to the current discussion in the Journal of Tourism Futures about the transformation and regenerative future of tourism.

Most of the 3D printing machines do not comply with the requirements of on-site, large-scale multi-story building construction. This paper aims to propose the conceptualization of a tower crane (TC)-based 3D printing controlled by artificial intelligence (AI) as the first step towards a large 3D printing development for multi-story buildings. It also aims to overcome the most important limitation of additive manufacturing in the construction industry (the build volume) by exploiting the most important machine used in the field: TCs. It assesses the technology feasibility by investigating the accuracy reached in the printing process.

The research is composed of three main steps: firstly, the TC-based 3D printing concept is defined by proposing an aero-pendulum extruder stabilized by propellers to control the trajectory during the extrusion process; secondly, an AI-based system is defined to control both the crane and the extruder toolpath by exploiting deep reinforcement learning (DRL) control approach; thirdly the proposed framework is validated by simulating the dynamical system and analysing its performance.

The TC-based 3D printer can be effectively used for additive manufacturing in the construction industry. Both the TC and its extruder can be properly controlled by an AI-based control system. The paper shows the effectiveness of the aero-pendulum extruder controlled by AI demonstrated by simulations and validation. The AI-based control system allows for reaching an acceptable tolerance with respect to the ideal trajectory compared with the system tolerance without stabilization.

In related literature, scientific investigations concerning the use of crane systems for 3D printing and AI-based systems for control are completely missing. To the best of the authors’ knowledge, the proposed research demonstrates for the first time the effectiveness of this technology conceptualized and controlled with an intelligent DRL agent.

The results provide the first step towards the development of a new additive manufacturing system for multi-storey constructions exploiting the TC-based 3D printing. The demonstration of the conceptualization feasibility and the control system opens up new possibilities to activate experimental research for companies and research centres.