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Building stock accounts for a major portion ofworldwide energy consumption and greenhouse gas emissions. Integrating resources, information and automation systems in aproper manner to achieve the required outcomes and meet therelevant regulatory standards for deep renovation and emergingenergy efficiency is a significant challenge. RINNO is a Horizon2020 project that aims to deliver a set of processes that, whenworking together, provide a system, repository, marketplace,and enabling workflow process for managing deep renovationprojects from inception to implementation. This paper presentsan overview of the RINNO project and, specifically, RINNO’sdesign for an open renovation platform for managing anddelivering deep renovation projects for residential buildingsbased on seven design principles. The proposed platform willbe developed, implemented and evaluated at four pilot sites withvarying construction, regulatory, market and climate contexts.

The delivery of construction projects in general can be complex and demanding and presents well-documented challenges to the control of cost, safety, and quality. This situation becomes even more challenging in the case of renovation projects due to the high level of interaction with occupants, especially when they remain in the building over the renovation period. The safety of project participants as well as that of occupants when they are present in the renovation site must be ensured. Although the planning and management of such projects can be greatly enhanced by exploiting some of the advantages of Building Information Modelling (BIM), the process of construction hazard identification and renovation scenarios assessment is still human-based and so requires considerable time and effort. Moreover, there is little research that addresses how hazard identification can best be represented and processed automatically in order to optimise and develop more effective strategies for managing construction projects, particularly those involving the systematic renovation of existing properties for better energy performance. Using BIM along with Artificial Intelligence (AI) tools could help in processing the massive amount of newly-available data and knowledge (e.g., feedback, images captured from smart devices, IoT sensors) that are increasingly obtainable. A prerequisite for doing so is the development of a dedicated ontology that would enable the formalisation of domain knowledge, including associated concepts, relations, and constraints that are specific to renovation project hazard. The authors propose an ontology and demonstrate its application by developing a knowledge-based system for application within the context of deep renovation projects that are part of a large European research project: the RINNO project., Transforming Construction with Reality Capture Technologies, 2022: The Digital Reality of Tomorrow

Heavy equipment represents a major cost element and a critical resource in large infrastructure projects. Automating the measurement of its productivity is important to remove the inaccuracies and inefficiencies of current manual measurement processes and to improve the performance of projects. Existing studies have prevalently focused on equipment activity recognition using mainly vision-based systems that require intrusive field installation and the application of more computationally demanding methods. This study aims to automate the measurement of equipment productivity using a combination of smartphone sensors to collect kinematic and noise data and deep learning algorithms. Different combination inputs and deep learning methods were implemented and tested in a real-world case study of a demolition activity. The results demonstrated a very high accuracy (99.78%) in measuring the productivity of the excavator. Construction projects can benefit from the proposed method to automate productivity measurement, identify equipment inefficiencies in near real time and inform corrective actions.

Building Information Modeling (BIM) is a technology revolution in the building industry where it has proven itself throughout the life cycle of a building. This reality has led in recent years to an extension of this concept to the Civil Engineering sector for infrastructure projects by deploying scientific research within universities or organized in international groups. The common goal is to facilitate the exchange of data and collaboration around these projects. In this context, our research focuses specifically on road projects and aims to propose a conceptual model for structuring a 3D BIM for this type of infrastructure. This conceptual model is based on the standards applicable in Morocco in addition to recommendations of BuildingSmart in terms of IFC standard. This article provides an exhaustive literature review on the recent advances on integrating BIM in the infrastructure sector. Then, a case study in Morocco is discussed to highlight the requirements and technical constraints that should be taken into consideration for the implementation of 3D BIM in roads infrastructure projects.

Measuring the productivity of earth moving equipment help to identify their inefficiencies and improve their performance; however, measurement processes are time and resource intensive. Current literature has foccussed on automating equipment activity capture but still lack adequate approaches for measurement of equipment productivity rates. Our contribution is to present a methodology for automating equipment productivity measurement using kinematic and noise data collected through smartphone sensors from within equipment and deep learning algorithms for recognizing equipment states. The testing of the proposed method in a real world case study demonstrated very high accuracy of 99.78 in measuring productivity of an excavator.

Building information modelling (BIM) is increasingly appropriate for infrastructure projects, and in particular for transport infrastructure. It is a digital solution that integrates the practices of the construction industry in facility management during the whole life cycle. This integration is possible through a single tool, which is the 3D digital model. Nevertheless, BIM standards, such as industry foundation classes, are still in the pipeline for infrastructure management. These standards do not fully meet the requirements of operation and maintenance of transport infrastructure. This paper shows how BIM could be implemented to address issues related to the operation and maintenance phase for transport infrastructure management. For this purpose, a new ontological approach, called Industry Foundation Classes for Operation and Maintenance of Infrastructures (IFCInfra4OM), is detailed. This ontology aims to standardise the use of building information modelling for operation and maintenance in road infrastructures. To highlight the interest of the proposed ontological approach, a building information model of a section on the A7 Agadir–Marrakech Highway in Morocco is produced according to IFCInfra4OM. The methodology is presented. The results obtained, including the IFCInfra4OM data model, are submitted. In the last section, an overview of the IFC extension approach is submitted.

The building stock accounts for a significant portion of worldwide energy consumption and greenhouse gas emissions. While the majority of the existing building stock has poor energy performance, deep renovation efforts are stymied by a wide range of human, technological, organisational and external environment factors across the value chain. A key challenge is integrating appropriate human resources, materials, fabrication, information and automation systems and knowledge management in a proper manner to achieve the required outcomes and meet the relevant regulatory standards, while satisfying a wide range of stakeholders with differing, often conflicting, motivations. RINNO is a Horizon 2020 project that aims to deliver a set of processes that, when working together, provide a system, repository, marketplace and enabling workflow process for managing deep renovation projects from inception to implementation. This paper presents a roadmap for an open renovation platform for managing and delivering deep renovation projects for residential buildings based on seven design principles. We illustrate a preliminary stepwise framework for applying the platform across the full-lifecycle of a deep renovation project. Based on this work, RINNO will develop a new open renovation software platform that will be implemented and evaluated at four pilot sites with varying construction, regulatory, market and climate contexts.

This paper proposes a new approach to creating Building Information (BIM) models of existing buildings from digitized images. This automatic approach is based on three main steps. The first involves extracting the useful information automatically from rasterized plans by using image processing techniques that include segmentation, filtering, dilation, erosion, and contour detection. This information feeds the knowledge base of an expert system for BIM model generation. In the second step, using the knowledge base of the expert system, the information required to inform the BIM model can be deduced. The range of information thus obtainable can be extended beyond the examples given. The paper concludes with a discussion of the final stage: the automatic generation of an Industry Foundation Classes (IFC) information model with all the desired geometric, physical and technical information. This can be accomplished by using one of the available open-source application program interfaces (APIs). This stage is currently work-in-progress and will be the subject of a future publication.

This paper presents a model (SLEUTH*) for projecting contrasting urban growth futures. It is derived from the SLEUTH model, which has been modified in order to incorporate an additional spatially explicit factor, and to be used in a fully controlled forecasting mode. Our aim is to spatially allocate urban growth, its amount and pattern, according to predefined prospective scenarios and assuming a non path-dependency approach. This modelling approach aims at being used under a "story and simulation" (SAS) approach, which constrains the model validation. To assess model efficiency, three types of tests have been undertaken: (1) sensitivity tests; (2) reproduction of known changes over a past period; and (3) simulation of changes that break trends. Results show that SLEUTH* conveniently simulates expected urban changes for exploring contrasting scenarios that are the basis for land planning strategies. Cet article présente un modèle de simulation spatiale (SLEUTH*) dédié à la projection de scenarios contrastés d’étalement urbain. Il hérite du modèle SLEUTH et les modifications apportées visent à intégrer un facteur additionnel de localisation des changements et à l’utiliser de façon contrôlée pour simuler ces projections. Le but est de pouvoir distribuer spatialement la croissance urbaine, en termes de superficie et de motif spatial, conformément à la définition de scénarios prospectifs normatifs afin de pouvoir les simuler dynamiquement. Ce modèle suppose donc d’être indépendant vis-à-vis des trajectoires passées afin d’être utilisé suivant une approche « récit et simulation ». Ce type d’approche engendre certaines contraintes pour la validation du modèle. Pour évaluer la performance du modèle, trois types de tests ont été réalisés : (1) des tests de sensibilité ; (2) la reproduction de changements observés sur une période passée ; et (3) démontrer son aptitude à simuler des ruptures dans les dynamiques d’étalement urbain. Les résultats montrent que SLEUTH* simule convenablement les changements (passés ou futurs) attendus et est approprié pour simuler des scénarios contrastés qui sont la base pour définir des stratégies de planification urbaine.

International audience; Societies have to both reduce their greenhouse gas emissions and undertake adaptation measures to limit the negative impacts of global warming on the population, the economy and the environment. Examining how best to adapt cities is especially challenging as urban areas will evolve as the climate changes. Thus, examining adaptation strategies for cities requires a strong interdisciplinary approach involving urban planners, architects, meteorologists, building engineers, economists, and social scientists. Here we introduce a systemic modelling approach to the problem.Our four-step methodology consists of: first, defining interdisciplinary scenarios; second, simulating the long-term evolution of cities on the basis of socio-economic and land-use models; third, calculating impacts with physical models (such as TEB), and; finally, calculating the indicators that quantify the effect of different adaptation policies. In the examples presented here, urban planning strategies are shown to have unexpected influence on city expansion in the long term. Moreover, the Urban Heat Island should be taken into account in operational estimations of building energy demands. Citizens’ practices seem to be an efficient lever for reducing energy consumption in buildings.Interdisciplinary systemic modelling appears well suited to the evaluation of several adaptation strategies for a very broad range of topics.

Using qualitative reasoning with geographic information, contrarily, for instance, with robotics, looks not only fastidious (i.e.: encoding knowledge Propositional Logics PL), but appears to be computational complex, and not tractable at all, most of the time. However, knowledge fusion or revision, is a common operation performed when users merge several different data sets in a unique decision making process, without much support. Introducing logics would be a great improvement, and we propose in this paper, means for deciding -a priori- if one application can benefit from a complete revision, under only the assumption of a conjecture that we name the "containment conjecture", which limits the size of the minimal conflicts to revise. We demonstrate that this conjecture brings us the interesting computational property of performing a not-provable but global, revision, made of many local revisions, at a tractable size. We illustrate this approach on an application., 14 pages