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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.

PurposeWhole building life cycle assessment (WBLCA) is a key methodology to reduce the environmental impacts in the building sector. Research studies usually face challenges in presenting comprehensive LCA results due to the complexity of assessments at the building level. There is a dearth of methods for the systematic evaluation and optimization of the WBLCA performance at the design stage. The study aims to develop a design optimization framework based on the proposed WBLCA method to evaluate and improve the environmental performance at the building level.Design/methodology/approachThe WBLCA development method is proposed with detailed processes based on the EN 15978 standard. The environmental product declaration (EPD) methods were adopted to ensure the WBLCA is comprehensive and reliable. Building information modeling (BIM) was used to ensure the building materials and assembly contributions are accurate and provide dynamic material updates for the design optimization framework. Furthermore, the interactive BIM-LCA calculation processes were demonstrated for measuring the environmental impacts of design upgrades. The TOPSIS-based LCA results normalization was selected to conduct the comparisons of various building design upgrades.FindingsThe case study conducted for a residential building showed that the material embodied impacts and the operational energy use impacts are the two critical factors that contribute 60–90% of the total environmental impacts and resource uses. Concrete and wood are the main material types accounting for an average of 65% of the material embodied impacts. The air and water heating for the house are the main energy factors, as these account for over 80% of the operational energy use. Based on the original WBLCA results, two scenarios were established to improve building performance through the design optimization framework.Originality/valueThe LCA results show that the two upgraded building designs create an average of 5% reduction compared with the original building design and improving the thermal performance of the house with more insulation materials does not always reduce the WBLCA results. The proposed WBLCA method can be used to compare the building-level environmental performances with the similar building types. The proposed framework can be used to support building designers to effectively improve the WBLCA performance.

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.

BIM (Building Information Modeling) is a new concept in construction industry and becomes popular worldwide in recent years. BIM is widely used by individuals, businesses and government agencies. It is applied in all phases of construction lifecycle from planning and designing to constructing and maintaining. It contains different structure types of buildings and infrastructures including water, refuse, electricity, gas, communication utilities, roads, railways, bridges, ports and tunnels. The application of BIM in the building industry is relatively widespread while the development of BIM method in the domain of infrastructure is yet in infancy. In this research, we would like to study the compatibility of BIM in modeling the bridge by using Autodesk Revit.

Interoperability is a key factor for BIM and helps to simplify the communication and the cooperation between different actors during the project life-cycle. IFC appears as the best option to exchange data between different software platforms. The purpose of this paper is to offer a proposal for exchanging data by merging IFC-based BIM models. We consider a BIM project model like a large system of multi modules where each part represents a structure (building, road, bridge and tunnel) and should be completed by an IFC model. This article presents a numerical tool which allows the co-design step through a Graphical User Interface (GUI) developed in Java language using the IfcToolsProjects libraries. This work is situated in non-automatic merging models being problematic. The proposal is clearly a co-design assistance. This Interface allows the merging of many buildings represented by different IFC files and produces a new IFC-based buildings complex model. The result is fully exploitable without any data loss. The new merging approach avoids the disadvantages of the “linking” function.

La modelisation prospective permet d'eclairer les reflexions des acteurs locaux en leur proposant un panel de futurs et d'alternatives possibles en matiere de planification et d'amenagement du territoire. Or, le modelisateur est souvent confronte aux limites des outils de simulation existants qui, calibres a partir de donnees passees, sont particulierement non adaptes a la spatialisation de scenarios prospectifs en rupture avec les tendances passees. L'objectif de cet article est de presenter un modele de simulation spatiale (SLEUTH*) dedie a la projection de scenarios contrastes et predefinis d’expansion urbaine. SLEUTH* constitue une version « scenario-dependante » issue de la modification du modele SLEUTH. Ce dernier, dont le fonctionnement est base sur un automate cellulaire, est un modele empirique, dynamique et spatialement explicite. Le choix du modele SLEUTH tient a sa capacite a simuler quatre formes d’expansion urbaine : expansion par diffusion, en continuite de l’urbain existant, le long des routes et par creation de nouveaux centres. Les ameliorations apportees permettent aux utilisateurs : (i) une maitrise de la quantite de changement de maniere exogene au modele et independante des tendances passees ; (ii) une maitrise des formes urbaines en offrant a l’utilisateur la possibilite de specifier les pourcentages contributifs de chacune des formes d'urbanisation adaptees a chaque scenario ; et (iii) l’integration d’un facteur additionnel de localisation des changements, en l’occurrence l’attractivite du territoire ou le prix des loyers. De plus, SLEUTH* est concu pour executer de facon automatique la spatialisation dynamique d'un scenario prospectif decline en sous- periodes presentant des dynamiques spatiales variables au cours du temps.

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.

International audience; A number of questions related to conservation biology and climate research require quantitative reconstructions of vegetation abundance or land-cover on a long-term historical perspective (e.g. Gaillard et al. 2010, Boyle et al., 2011, Kaplan et al., 2011). In particular, long-term changes in plant composition and abundance play a critical role in land coverclimate feedbacks (e.g. Wramneby et al. 2010) and, along with spatial distribution of species and habitats, they are probably one of the major factors affecting floristic and plant habitat diversity through time (e.g. Jeffers et al. 2011, Willis et al. 2010, Berglund et al., 2008, Willis and Birks, 2006). Jeffers et al. (2011) showed that abrupt changes in environmental conditions over time lead to similarly abrupt changes in tree-grass interaction outcomes. However, traditional percentage pollen records do not allow measuring the true extent of changes in e.g. the relationship between trees’ and herbs’ abundances/cover or other characteristics of plant spatial distributions through time (e.g. Gaillard et al., 2010). In the C3A project, questions related to the history of landscape fragmentation, and its consequences in terms of e.g. past changes in floristic diversity, require that the true abundance of the major plants composing the mountain forest and mixed forest-savanna landscapes of the past is quantified as precisely as possible before anything can be attempted in terms of reconstruction of past spatial distribution of plants or vegetation units, and changes in floristic diversity. In order to attempt a first quantification of Holocene changes in the abundance of major plant taxa of the mountain forest and savannas in Cameroon, we applied the Landscape Reconstruction Algorithm (LRA; Sugita 2007a and b) using five pollen records from contrasting vegetation regions today, i.e. one large (47 ha) and two small (8, and 25 ha) lakes in the savannas, and one large (198 ha) and one small (16.5 ha) bogs in the mountain forests. So far, the LRA was tested only in Europe using modern and historical pollen-vegetation datasets, and applied on fossil pollen records (e.g. Gaillard et al., 2010, Nielsen et al., 2011). The application of the LRA and its two models REVEALS (Regional Etimates of VEgetation Abundance from Large Sites) and LOVE (LOcal Vegetation Estimates) requires values of pollen productivity and fall speed of pollen (FSP) for the plant taxa to be reconstructed. Pollen productivity (PP) can be estimated using modern pollen data and related vegetation inventories and models of the pollen-vegetation relationship, i.e. the Extended R-Value model (ERV-model, e.g. Prentice and Parsons, 1983). Measurements of fall speed of pollen do exist for a number of temperate plant species, but none exist for tropical species. However, reasonable estimates can be obtained by the Stoke’s law on the basis of pollen grains’ mean size (e.g. Broström et al., 2008). FSP and PPs were calculated for only a few taxa of south African savannas (Duffin et al., 2008). We estimated PP for 16 major taxa on the basis of modern pollen data and related vegetation inventories at 21 sites in mountain forests and mixed forest-savannas. Preliminary FSPs for the 16 taxa were calculated on mean values of pollen sizes from the literature and few measures on slides from reference collections. First results indicate that, relatively to Gramineae (grasses), tree taxa such as Schefflera, Combretaceae and Syzygium have very high to high PPEs, while Asteraceae, Urticaceae, Podocarpus, Pavetta, and Bridelia have low to very low PPEs. Alchornea andLophira have PPEs comparable to Gramineae. This implies that Schefflera will tend to be overrepresented while many other trees such as Podocarpus and Bridelia will often be underrepresented by pollen percentages. Poaceae will, as a consequence, be overrepresented compared to trees such as Podocarpus and Pavetta. We applied the LRA using the two large lakes to estimate past regional vegetation abundance (REVEALS model), and the three small sites to estimate the local vegetation abundance (LOVE model). Preliminary results show that the relevant source area of pollen (RSAP sensu Sugita 1994) of the three small sites varies with time between a 500 m -and 5000 m - radius area around the sites, which is the smallest area for which the LRA estimates of vegetation cover are reliable. The LRA reconstruction indicates that the sites located today in mixed forest-savanna were significantly more forested in the past than pollen percentages suggest, in particular before 3000 BP, but also 1500-2000 BP. Podocarpus and Alchornea in particular probably covered more land than pollen assemblages alone would suggest, while grasses covered much less land than its representation in pollen assemblages. Moreover, the change around 3000 BP is more spectacular when LRA estimates are considered. At the sites located in mountain forests today, the regional landscape was dominated by trees during most of the Holocene (REVEALS reconstruction at the large site), while tree pollen decreased in abundance around 3000 BP at the small site (LRA reconstruction). Podocarpus, Schefflera and Alchornea were the major tree pollen taxa. Before 3000 BP, Schefflera is dominant in the pollen assemblage, while Podocarpus is dominant in the LRA reconstruction. Alchornea is slightly better represented in the land cover than in the pollen assemblages. The LRA estimates indicate a later and more rapid increase of Gramineae from 3500 to 4000 BP than the pollen percentages (increase from 4000 BP). After 3000 BP, Schefflera is the dominant tree, although with less percentage cover than pollen percentages. The LRA reconstruction suggests that Podocarpus percentage cover dropped between 3000 and 2500 BP, but reached very low values later than indicated by the pollen percentages , i.e. ca. 2000 BP instead of 2500 BP. In conclusion, the major effect of the LRA correction is to 1) increase the tree/herb relationship before 3000 BP, 2) amplify the extent and rate of increase in Gramineae at the expense of trees around 3000 BP, and 3) inverse the Podocarpus/Schefflera relationship. New ERV-model runs to estimate PPs are in progress. They are based on a revised selection of 20 major taxa and on FSPs calculated from a large number of measurements on modern and fossil pollen grains. New LRA runs are planed with the objective to evaluate the first LRA reconstructions using a larger choice of FSPs and PPEs.