Using the Knowledge Transfer Partnership model as a method of transferring BIM and Lean process related knowledge between academia and industry: A Case Study Approach

This paper looks at the vehicle of the Knowledge Transfer Partnership (KTP) between academia and business and how successful it is in reaching its range of objectives and developing theoretical and practical educational materials for BIM curriculums. The KTP operates by helping businesses improve their competitiveness and productivity through the better use of knowledge, technology and skills that reside within the UK knowledge base. At the same time, it also helps to increase the business relevance of knowledge base research and teaching for the academic institutions. For this paper, the KTP project between the University of Salford and John McCall Architects (JMA) in Liverpool is reviewed. This two year KTP focused on the implementation of BIM and Lean principles to JMA’s architectural practice in social housing sector. The KTP project is 70% Government funded and 30% funded by JMA and undertaken under the Technology Strategy Board programme, enabling innovation in business. The initial aims and objectives of the KTP are assessed and evaluated against the actual knowledge transfer and implementation and the final outcomes of the KTP for the University, JMA and the KTP associate are highlighted

Simply BIM for facilities management: how to use building information model for typical facilities management tasks in a practical way

Building Information Modelling (BIM) as a new evolutionary way of working methodology for the building industry enables collaboration and improved communication, underpinned by the digital technologies, which unlock more efficient methods of designing, delivering and maintaining physical built assets. BIM uptake is picking up lately but mainly concentrating on design and construction with less emphasis on facilities management. Claims in the literature are asserting that the BIM use for FM requires Integrated BIM use, classified as maturity Level 3, with a life-cycle perspective. However, this book explains hands-on case study research of BIM modelling and its use for FM tasks (soft and hard tasks) as if BIM modelling for a new building to show that use of BIM can be practically usable for facilities management simply via simply BIM modelling. With the approach in the book about BIM use for FM, the authors encourage researchers and practitioners to explore practicing BIM at the modelling level, which is believed that the BIM use for FM and BIM modelling for existing buildings will be intensified

Housing cycles in the UK: a historical and empirical investigation

The study looks at the characteristics of upswings and downswings for UK housing cycles. Specifically, the purpose of this paper is to empirically analyse cycles in house prices and housing affordability on the characteristics of persistence, magnitude and severity. The paper draws upon the triangular methodology of cycles and utilises housing data from the last three decades. From an empirical perspective, the study obtained four main results. First, the graphical trajectory of cycles in house price and housing affordability is highly synchronized. Second, upturns in both cycles tend to be longer than downturns on average. Third, the recent upturn in house prices and housing affordability is characterised by larger duration, magnitude and severity than the earlier case. Fourth, the latest downturn in both cycles is highly synchronised in terms of time occurrence, persistence, magnitude and severity; in addition, in both cases, the latest downturn is considerably smaller than the previous one. The study additionally indicates that on average the length of a complete house price and housing affordability cycle is 19 years on a peak-to-peak basis. This paper is essentially exploratory and raises a number of questions for further investigation. Future research should, first, arrive at a more nuanced definition of affordability and, second, examine causality. The fact that two phenomena appear to have some significant synchronicity is not an indication that they are interdependent, although logic would suggest they might be. This paper is essentially exploratory and raises a number of questions for further investigation. Future research should, first, arrive at a more nuanced definition of affordability and, second, examine causality. The fact that two phenomena appear to have some significant synchronicity is not an indication that they are interdependent, although logic would suggest they might be. This is among the few papers that analyses cycles in UK house prices. It is the first study that draws attention to the housing affordability cycle and the first to compare cycles in house prices with cycles in housing affordability

Lean Management Framework for Healthcare Facilities Integrating BIM, BEPS and Big Data Analytics

An increase in the usage of information and communication technologies (ICT) and the Internet of Things (IoT) in Facility Management (FM) induces a huge data stack. Even though these data bring opportunities such as cost savings, time savings, increase in user comfort, space optimization, energy savings, inventory management, etc., these data sources cannot be managed and manipulated effectively to increase efficiency at the FM stage. In addition to data management issues, FM practices, or developed solutions, need to be supported with the implementation of lean management philosophy to reveal organizational and managerial wastes. In the literature, some researchers performed studies about awareness about building information modeling (BIM)-FM, and FM-related data management problems in terms of lean philosophy. However, the comprehensive solution for effective FM has not been investigated with the application of lean management philosophy yet. Therefore, this study aims to develop an FM framework for healthcare facilities by considering lean management philosophy since more stable workflow, continuous improvement, and creating more value to customers will help to deliver a more acceptable solution for the FM industry. Within this context, the integration of BIM, Building Energy Performance Simulations, and Big Data Analytics are proposed as a solution. In the study, the Design Science Research (DSR) methodology was followed to develop the FM framework. Depending on the DSR methodology, two scenarios were used to investigate the issue in a real healthcare facility and develop the FM framework. The developed framework was evaluated by four experts, and the revisions of the proposed framework were realized

Identification and Prioritization of Key Performance Indicators for the Construction Small and Medium Enterprises

The central purpose of this study is to propose a set of key performance indicators (KPIs) to measure the performance of construction small and medium enterprises (SMEs) that have been ignored in the performance management literature so far. Secondly, this study aims to determine the most crucial KPIs by using the fuzzy VIKOR method to improve cost-effectiveness in the performance measurement of construction SMEs. At the first stage of this study, KPIs proposed by the existing studies were identified via a literature survey. Then, the KPIs extracted from the literature survey were verified, and eight new KPIs were proposed as a result of focus group discussions with 12 participants who are owners/managers of construction SMEs. Additionally, the Balanced Scorecard (BSC) was modified in line with the needs of construction SMEs, and each KPI was grouped into a BSC perspective. A questionnaire survey followed this grouping to gather data associated with the KPIs. Based on these data, KPIs were prioritized by using the fuzzy VIKOR. It is found out that external indicators such as “effectiveness of monitoring market conditions” are determined as the most important KPIs, in contrast to the findings in the studies about large-scale companies. Furthermore, “Attracting new customers”; “Reliability of financial performance” and, “Competency of managers” are identified as important indicators. Four KPIs proposed by experts during the focus group discussion are placed among the most important KPIs, which highlights the need for a specific performance measurement system (PMS) for construction SMEs

Determination of Business Intelligence and Analytics-Based Healthcare Facility Management Key Performance Indicators

The use of digital technologies such as Internet of Things (IoT) and smart meters induces a huge data stack in facility management (FM). However, the use of data analysis techniques has remained limited to converting available data into information within activities performed in FM. In this context, business intelligence and analytics (BI&A) techniques can provide a promising opportunity to elaborate facility performance and discover measurable new FM key performance indicators (KPIs) since existing KPIs are too crude to discover actual performance of facilities. Beside this, there is no comprehensive study that covers BI&A activities and their importance level for healthcare FM. Therefore, this study aims to identify healthcare FM KPIs and their importance levels for the Turkish healthcare FM industry with the use of the AHP integrated PROMETHEE method. As a result of the study, ninety-eight healthcare FM KPIs, which are categorized under six categories, were found. The comparison of the findings with the literature review showed that there are some similarities and differences between countries’ FM healthcare ranks. Within this context, differences between countries can be related to the consideration of limited FM KPIs in the existing studies. Therefore, the proposed FM KPIs under this study are very comprehensive and detailed to measure and discover healthcare FM performance. This study can help professionals perform more detailed building performance analyses in FM. Additionally, findings from this study will pave the way for new developments in FM software and effective use of available data to enable lean FM processes in healthcare facilities

Urban information model for city planning

City planning is a complex task and therefore needs to consider the interplay between multi-aspects of a city, for example, transport, pollution, and crime. A city model is important to representing urban issues in a clear manner to the relative stakeholders. Although some city models have been used in the planning process, they are often based on narrow data sets. When sustainability and the quality of urban life generally is considered a more holistic analysis of city issues during the planning process is needed. It calls for city models to be based on integrated data sets. The paper describes the concept and challenges of nD urban information model. The research work on how to develop an nD urban information model to accommodate data sets relevant to different aspects of city planning is presented

Machine and deep learning implementations for heritage building information modelling : a critical review of theoretical and applied research

Research domain and Problem: HBIM modelling from point cloud data has become a crucial research topic in the last decade since it is potentially considered as the central data model paving the way for the digital heritage practice beyond digitization. Reality Capture technologies such as terrestrial laser scanning, drone-mounted LiDAR sensors and photogrammetry enable the reality capture with a sub-millimetre accurate point cloud file that can be used as a reference file for Heritage Building Information Modelling (HBIM). However, HBIM modelling from the point cloud data of heritage buildings is mainly manual, error-prone, and time-consuming. Furthermore, image processing techniques are insufficient for classification and segmentation of point cloud data to speed up and enhance the current workflow for HBIM modelling. Due to the challenges and bottlenecks in the scan-to-HBIM process, which is commonly criticized as complex with its bespoke requirements, semantic segmentation of point clouds is gaining popularity in the literature. Research Aim and Methodology: Therefore, this paper aims to provide a thorough critical review of Machine Learning and Deep Learning methods for point cloud segmentation, classification, and BIM geometry automation for cultural heritage case study applications. Research findings: This paper files the challenges of HBIM practice and the opportunities for semantic point cloud segmentation found across academic literature in the last decade. Beyond definitions and basic occurrence statistics, this paper discusses the success rates and implementation challenges of machine and deep learning classification methods. Research value and contribution: This paper provides a holistic review of point cloud segmentation and its potential for further development and application in the Cultural Heritage sector. The critical analysis provides insight into the current state-of-the-art methods and advises on their suitability for HBIM projects. The review has identified highly original threads of research, which hold the potential to significantly influence practice and further applied research

Implementing PointNet for point cloud segmentation in the heritage context

Automated Heritage Building Information Modelling (HBIM) from the point cloud data has been researched in the last decade as HBIM can be the integrated data model to bring together diverse sources of complex cultural content relating to heritage buildings. However, HBIM modelling from the scan data of heritage buildings is mainly manual and image processing techniques are insufficient for the segmentation of point cloud data to speed up and enhance the current workflow for HBIM modelling. Artificial Intelligence (AI) based deep learning methods such as PointNet are introduced in the literature for point cloud segmentation. Yet, their use is mainly for manufactured and clear geometric shapes and components. To what extent PointNet based segmentation is applicable for heritage buildings and how PointNet can be used for point cloud segmentation with the best possible accuracy (ACC) are tested and analysed in this paper. In this study, classification and segmentation processes are performed on the 3D point cloud data of heritage buildings in Gaziantep, Turkey. Accordingly, it proposes a novel approach of activity workflow for point cloud segmentation with deep learning using PointNet for the heritage buildings. Twenty-eight case study heritage buildings are used, and AI training is performed using five feature labelling for segmentation namely, walls, roofs, floors, doors, and windows for each of these 28 heritage buildings. The dataset is divided into clusters with 80% training dataset and 20% prediction test dataset. PointNet algorithm was unable to provide sufficient accuracy in segmenting the point clouds due to deformation and deterioration on the existing conditions of the heritage case study buildings. However, if PointNet algorithm is trained with the restitution-based heritage data, which is called synthetic data in the research, PointNet algorithm provides high accuracy. Thus, the proposed approach can build the baseline for the accurate classification and segmentation of the heritage buildings