The present paper focuses on the architectural and constructional features required to ensure that building envelope renovation are safe, functional, and adaptable to the building stock, with particular focus on “plug and play” modular facade construction systems. It presents the design of one such system and how it addresses these issues. The outcome of early-stage functional test with a full-scale mock-up system, as well as its applicability to a real construction project is presented. It is found crucial to obtain high quality information about the status of the existing façade with the use of modern technologies such as topographic surveys or 3D scans and point cloud. Detailed design processes are required to ensure the compatibility of manufacture and installation tolerances, along with anchor systems that deliver flexibility for adjustment, and construction processes adapting standard installation methods to the architectural particularities of each case that may hinder its use or require some modification in each situation. This prefabricated plug and play modular system has been tested by reproducing the holistic methodology and new technologies in the market by means of real demonstrators. When compared to more conventional construction methods, this system achieves savings in a real case of 50% (time), 30% (materials) and 25% (waste), thus achieving significant economic savings.

Construction sites are highly unpredictable environments involving a wide variety of stakeholders with complex information exchanges, which lead to the well-known inefficiencies and unproductivity of the construction sector. The adoption of Building Digital Twins (BDT) in the construction site is a promising solution to this issue, by automating data acquisition and knowledge extraction processes and providing what-if scenario simulation capabilities. Furthermore, the current research sets the principles to define, replicate, and scale-up the architecture of a Building Digital Twin Platform (BDTP), conceived as a scalar ecosystem, which allows to seamlessly manage on-site construction processes, integrating cross-cutting domains for the construction site optimization (Progress monitoring, Quality control, Operational Health and Safety, Equipment control, and Production planning). The starting point of the research is a comprehensive diagnosis of on-site process inefficiencies and the barriers to its digitalization leading to the user requirements, which have been underpinned by questionnaires and interviews addressed within an open innovation user-centered approach around Living Labs. The research has been conceived following the Design Science Research (DSR) methodology and based on the Plan-Do-Check-Act (PDCA) analysis for the continuous improvement of the construction process. By means of the adoption of the standard Business Process Model and Notation (BPMN), based on the BDTP architecture, the research has resulted in BPMN workflows stemmed from the Digital Twin (DT) where the DT itself is an actor in a service-oriented data-exchange workflow. Moreover, the use of a BDTP can pave the way for the transition from user-driven construction management to hybrid management, coexisting with both human and digital actors and merging expert knowledge with artificial intelligence techniques.

Industrial buildings are a key element in the industrial fabric, and their maintenance is essential to ensure their proper functioning and avoid disruptions and costly economic losses. Continuous maintenance based on an accurate diagnosis makes it possible to meet the challenges of aging infrastructures, which demands a reliable data-based assessment for maintenance management implementing corrective and preventive actions, according to the damage criticality. This paper researches an innovative digitalized process for the inspection and diagnosis of industrial buildings, which leads to categorizing and prioritizing maintenance actions in an objective and cost-effective way from the inspection data. The process integrates some technical developments carried out in this work, aimed to automate the workflow: the drone-based inspection, the building condition assessment from the definition of a standardized construction pathology library, and a visual analysis of pathology evolution based on photogrammetry. The use of drones for digitalized inspection involves some challenges related to the positioning of the drone for damage localization, which has been herein overcome by developing a geo-annotation system for image acquisition. This system has also enabled the capture of geo-located images intended to generate 3D photogrammetric models for quantifying the pathological process evolution. Moreover, the assessment procedure outlined through multi-criteria decision-making methodology MIVES establishes a single criterion to automatically weight the relative importance of the damage defined in the library. As a result, this procedure yields the so-called Intervention Urgency Index (IUI), which allows prioritizing the maintenance actions associated with the damage while also considering economic criteria. In such a way, the overall process aims to increase reliability and consistency in the results of inspection and diagnosis needed for the effective maintenance management of industrial buildings.

The Galapagos Islands are undeniably a highly attractive ecosystem for scientists worldwide. However, the energy efficiency and sustainability aspects of their building stock have not yet been studied in depth, which directly hinders the achievement of sustainability goals for the Archipelago, such as reducing resource consumption, minimizing emissions, and improving overall comfort in buildings. Addressing these issues is critical to preserving the islands’ unique ecosystem, as current construction practices are unsustainable and exacerbate environmental pressures, causing over-consumption of local resources and upsetting the delicate ecological balance that sustains this fragile environment. In line with the National Energy Efficiency Plan promoted by the Government of Ecuador for the Archipelago, this study provides transparent and reliable information and data on the building stock of the islands. This work quantifies the impact of buildings on the use of resources and analyses the potential savings of different strategies for reducing greenhouse gas emissions. Various representative typologies are established based on the collection of architectural, construction, and usage information. For each of these typologies, various energy models are developed to establish the baseline and to analyse the demand and comfort of the buildings under different renovation scenarios in order to validate the sustainable construction strategies to be implemented. Moreover, new standards are also defined in order to reduce energy and water consumption and increase indoor air quality and comfort in buildings. In an attempt to generate evidence and facilitate the replication and implementation of sustainable construction standards, three Living Labs (LLs) are created to validate different strategies and technological solutions in different locations, according to the defined standards: a school in Santa Cruz, a hotel in Isabela, and a residential building in San Cristóbal. The findings highlight the effectiveness of specific energy-saving strategies and water conservation measures validated through Living Labs implemented in different locations across the islands. Furthermore, the knowledge generated is transferred through local training of the agents of the construction chain and administration.

The maintenance of industrial buildings is essential to ensure the operability, safety, and efficiency of these facilities, which are key pillars of the industrial sector. Proper maintenance management has a direct impact on productivity and the continuity of operational processes. However, managing the maintenance of these built assets pose significant challenges, particularly in terms of efficiency in inspection, diagnosis, and the planning of corrective and preventive activities, requiring substantial investment to preserve functionality and prevent disruptions. To address these challenges, this paper presents the development of an intelligent system for advanced damage diagnosis in industrial buildings. The system integrates the MIVES multi-criteria decision-making methodology to objectively assess the structural condition of built assets, incorporating complementary technologies: drones for inspection; artificial intelligence (AI) for data analysis and automatic damage detection; extended reality (XR) for the relocalisation of previously captured environments and the spatial positioning of identified damage; and radiance fields (such as 3D Gaussian Splatting) for 3D scene reconstruction and digital twin generation. A prototype workflow for damage detection was developed by training deep-learning algorithms trained on field images of pathologies in industrial built assets and generating digital twins of the facility using photogrammetry techniques and 3D Gaussian Splatting. This workflow supports the detection and visualization of potential pathologies and provides the data necessary for their objective assessment within the MIVES-base condition condition-assessment framework. These developments are integrated into an application that allows managers and asset owners to access and track inspection information through structured damage records, spatial localization, and digital visualization tools that support maintenance planning and decision-making.