DIMCloud: a distributed framework for district energy simulation and management

To optimize energy consumption, it is needed to monitor real-time data and simulate all energy flows. In a city district context, energy consumption data usually come from many sources and encoded in different formats. However, few models have been proposed to trace the energy behavior of city districts and handle related data. In this article, we introduce DIMCloud, a model for heterogeneous data management and integration at district level, in a pervasive computing context. Our model, by means of an ontology, is able to register the relationships between different data sources of the district and to disclose the sources locations using a publish-subscribe design pattern. Furthermore, data sources are published as Web Services, abstracting the underlying hardware from the user’s point-of-view

A scalable middleware-based infrastructure for energy management and visualization in city districts

Following the Smart City views, citizens, policy makers and energy distribution companies need a reliable and scalable infrastructure to manage and analyse energy consumption data in a city district context. In order to move forward this view, a city district model is needed, which takes into account different data-sources such as Building Information Models, Geographic Information Systems and real-time information coming from heterogeneous devices in the district. The Internet of Things paradigm is creating new business opportunities for low-cost, low-power and high-performance devices. Nevertheless, because of the "smart devices" heterogeneity, in order to provide uniform access to their functionalities, an abstract point of view is needed. Therefore, we propose an distributed software infrastructure, exploiting service-oriented middleware and ontology solutions to cope with the management, simulation and visualization of district energy data

Extending Building Information Modeling (BIM) interoperability to geo-spatial domain using semantic web technology

As Building Information Modeling (BIM) applications become more sophisticated and used within other knowledge domains, the limitations of existing data exchange and sharing methods become apparent. The integration of BIM and Geographic Information System (GIS) can offer substantial benefits to manage the planning process during the design and construction stages. Currently, building (and geospatial) data are shared between BIM software tools through a common data format, such as Industry Foundation Classes (IFC). Because of the diversity and complexity of domain knowledge across BIM and GIS systems, however, these syntactic approaches are not capable of overcoming semantic heterogeneity. This study uses semantic web technology to ensure the highest level of interoperability between existing BIM and GIS tools. The proposed approach is composed of three main steps; ontology construction, semantic integration through interoperable data formats and standards, and query of heterogeneous information sources. Because no application ontology is available to encompass all IFC classes with different attributes, we first develop an IFC-compliant ontology describing the hierarchy structure of BIM objects. Then, we can translate the building's elements and GIS data into semantic web standard formats. Once the information has been gathered from different sources and transformed into an appropriate semantic web format, the SPARQL query language is used in the last step to retrieve this information from a dataset. The completeness of the methodology is validated through a case study and two use case examples.Ph.D

A BIM - GIS Integrated Information Model Using Semantic Web and RDF Graph Databases

In recent years, 3D virtual indoor and outdoor urban modelling has become an essential geospatial information framework for civil and engineering applications such as emergency response, evacuation planning, and facility management. Building multi-sourced and multi-scale 3D urban models are in high demand among architects, engineers, and construction professionals to achieve these tasks and provide relevant information to decision support systems. Spatial modelling technologies such as Building Information Modelling (BIM) and Geographical Information Systems (GIS) are frequently used to meet such high demands. However, sharing data and information between these two domains is still challenging. At the same time, the semantic or syntactic strategies for inter-communication between BIM and GIS do not fully provide rich semantic and geometric information exchange of BIM into GIS or vice-versa. This research study proposes a novel approach for integrating BIM and GIS using semantic web technologies and Resources Description Framework (RDF) graph databases. The suggested solution's originality and novelty come from combining the advantages of integrating BIM and GIS models into a semantically unified data model using a semantic framework and ontology engineering approaches. The new model will be named Integrated Geospatial Information Model (IGIM). It is constructed through three stages. The first stage requires BIMRDF and GISRDF graphs generation from BIM and GIS datasets. Then graph integration from BIM and GIS semantic models creates IGIMRDF. Lastly, the information from IGIMRDF unified graph is filtered using a graph query language and graph data analytics tools. The linkage between BIMRDF and GISRDF is completed through SPARQL endpoints defined by queries using elements and entity classes with similar or complementary information from properties, relationships, and geometries from an ontology-matching process during model construction. The resulting model (or sub-model) can be managed in a graph database system and used in the backend as a data-tier serving web services feeding a front-tier domain-oriented application. A case study was designed, developed, and tested using the semantic integrated information model for validating the newly proposed solution, architecture, and performance

BIM-based software for construction waste analytics using artificial intelligence hybrid models

The Construction industry generates about 30% of the total waste in the UK. Current high landfill cost and severe environmental impact of waste reveals the need to reduce waste generated from construction activities. Although literature reveals that the best approach to Construction Waste (CW) management is minimization at the design stage, current tools are not robust enough to support architects and design engineers. Review of extant literature reveals that the key limitations of existing CW management tools are that they are not integrated with the design process and that they lack Building Information Modelling (BIM) compliance. This is because the tools are external to design BIM tools used by architects and design engineers. This study therefore investigates BIM-based strategies for CW management and develops Artificial Intelligent (AI) hybrid models to predict CW at the design stage. The model was then integrated into Autodesk Revit as an add-in (BIMWaste) to provide CW analytics. Based on a critical realism paradigm, the study adopts exploratory sequential mixed methods, which combines both qualitative and quantitative methods into a single study. The study starts with the review of extant literature and (FGIs) with industry practitioners. The transcripts of the FGIs were subjected to thematic analysis to identify prevalent themes from the quotations. The factors from literature review and FGIs were then combined and put together in a questionnaire survey and distributed to industry practitioners. The questionnaire responses were subjected to rigorous statistical process to identify key strategies for BIM-based approach to waste efficient design coordination. Results of factor analysis revealed five groups of BIM strategies for CW management, which are: (i)improved collaboration for waste management, (ii)waste-driven design process and solutions, (iii)lifecycle waste analytics, (iv) Innovative technologies for waste intelligence and analytics, and (v)improved documentation for waste management. The results improve the understanding of BIM functionalities and how they could improve the effectiveness of existing CW management tools. Thereafter, the key strategies were developed into a holistic BIM framework for CW management. This was done to incorporate industrial and technological requirements for BIM enabled waste management into an integrated system.The framework guided the development of AI hybrid models and BIM based tool for CW management. Adaptive Neuro-Fuzzy Inference System (ANFIS) model was developed for CW prediction and mathematical models were developed for CW minimisation. Based on historical Construction Waste Record (CWR) from 117 building projects, the model development reveals that two key predictors of CW are “GFA” and “Construction Type”. The final models were then incorporated into Autodesk Revit to enable the prediction of CW from building designs. The performance of the final tool was tested using a test plan and two test cases. The results show that the tool performs well and that it predicts CW according to waste types, element types, and building levels. The study generated several implications that would be of interest to several stakeholders in the construction industry. Particularly, the study provides a clear direction on how CW management strategies could be integrated into BIM platform to streamline the CW analytics

Smart data management with BIM for Architectural Heritage

In the last years smart buildings topic has received much attention as well as Building Information Modelling (BIM) and interoperability as independent fields. Linking these topics is an essential research target to help designers and stakeholders to run processes more efficiently. Working on a smart building requires the use of Innovation and Communication Technology (ICT) to optimize design, construction and management. In these terms, several technologies such as sensors for remote monitoring and control, building equipment, management software, etc. are available in the market. As BIM provides an enormous amount of information in its database and theoretically it is able to work with all kind of data sources using interoperability, it is essential to define standards for both data contents and format exchange. In this way, a possibility to align research activity with Horizon 2020 is the investigation of energy saving using ICT. Unfortunately, comparing the Architecture Engineering and Construction (AEC) Industry with other sectors it is clear how in the building field advanced information technology applications have not been adopted yet. However in the last years, the adoption of new methods for the data management has been investigated by many researchers. So, basing on the above considerations, the main purpose of this thesis is investigate the use of BIM methodology relating to existing buildings concerning on three main topics: • Smart data management for architectural heritage preservation; • District data management for energy reduction; • The maintenance of highrises. For these reasons, data management acquires a very important value relating to the optimization of the building process and it is considered the most important goal for this research. Taking into account different kinds of architectural heritage, the attention is focused on the existing and historical buildings that usually have characterized by several constraints. Starting from data collection, a BIM model was developed and customized in function of its objectives, and providing information for different simulation tests. Finally, data visualization was investigated through the Virtual Reality(VR) and Augmented Reality (AR). Certainly, the creation of a 3D parametric model implies that data is organized according to the use of individual users that are involved in the building process. This means that each 3D model can be developed with different Levels of Detail/Development (LODs) basing on the goal of the data source. Along this thesis the importance of LODs is taken into account related to the kind of information filled in a BIM model. In fact, basing on the objectives of each project a BIM model can be developed in a different way to facilitate the querying data for the simulations tests.\ud The three topics were compared considering each step of the building process workflow, highlighting the main differences, evaluating the strengths and weaknesses of BIM methodology. In these terms, the importance to set a BIM template before the modelling step was pointed out, because it provides the possibility to manage information in order to be collected and extracted for different purposes and by specific users. Moreover, basing on the results obtained in terms of the 3D parametric model and in terms of process, a proper BIM maturity level was determined for each topic. Finally, the value of interoperability was arisen from these tests considering that it provided the opportunity to develop a framework for collaboration, involving all parties of the building industry

Implementation of smart devices in the construction industry

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.The construction industry has a fragmented nature, which accounts for the highest degree of decentralisation of information and the highest mobile content access. The exchange of information made possible by smart devices. This creates an opportunity to enhance productivity and communication among stakeholders of the construction industry. Firstly, this thesis explored the concept of smart devices. Secondly, the drivers, challenges and Critical Success Factors for implementing smart devices were investigated. This study adopted a qualitative approach using semi-structured interviews. A total of Thirty-nine interviewees which includes professionals from the construction sector of the Dominican Republic (DR) and the United Kingdom (UK) were interviewed. Thematic analysis was used to analyse the collected data. The drivers for the adoption of smart devices were grouped into internal and external drivers. The challenges found in the interviews were grouped into three categories, namely, economic, cultural and technological. The Critical Success Factors (CSFs) for implementing smart devices in the construction industry are leadership, training and development, organisational culture, technology awareness, cost, company size and usability. These findings were used to develop a strategic framework which has two sub-frameworks. This study concluded that a specific culture must be adopted on behalf of the government and construction companies to successfully adopt smart devices. Furthermore, this investigation found various similarities and differences regarding the drivers, challenges and CSFs for implementing smart devices in the UK and the DR. This study recommends integrating smart devices in data collection techniques in academia. Also, for construction companies to embrace technological innovation it is recommended to be willing to start new ventures, to be open to the participation of all members of the company, and be creative and client-oriented.Ministerio de Educacion Superior, Ciencia y Tecnología (MESCyT) - Ministry of Higher education, Science and technology of the Dominican Republic