From crowd to cloud: simplified automatic reconstruction of digital building assets for facility management

Purpose The purpose of this paper is to investigate the feasibility of an end-to-end simplified and automated reconstruction pipeline for digital building assets using the design science research approach. Current methods to create digital assets by capturing the state of existing buildings can provide high accuracy but are time-consuming, expensive and difficult. Design/methodology/approach Using design science research, this research identifies the need for a crowdsourced and cloud-based approach to reconstruct digital building assets. The research then develops and tests a fully functional smartphone application prototype. The proposed end-to-end smartphone workflow begins with data capture and ends with user applications. Findings The resulting implementation can achieve a realistic three-dimensional (3D) model characterized by different typologies, minimal trade-off in accuracy and low processing costs. By crowdsourcing the images, the proposed approach can reduce costs for asset reconstruction by an estimated 93% compared to manual modeling and 80% compared to locally processed reconstruction algorithms. Practical implications The resulting implementation achieves “good enough” reconstruction of as-is 3D models with minimal tradeoffs in accuracy compared to automated approaches and 15× cost savings compared to a manual approach. Potential facility management use cases include the issue and information tracking, 3D mark-up and multi-model configurators. Originality/value Through user engagement, development, testing and validation, this work demonstrates the feasibility and impact of a novel crowdsourced and cloud-based approach for the reconstruction of digital building assets.

INTEGRATION OF BIM AND MOBILE AUGMENTED REALITY IN THE AECO DOMAIN

BIM (Building Information Modeling) is increasingly present in a wide range of applications (architecture, engineering, construction, land use planning, utility management, etc.). BIM allows better management of projects through precise planning, communication and collaboration between several stakeholders as well as facilitating the monitoring of project operations. The emergence of Augmented Reality (AR) technology allows the superposition of (2D, 3D) information directly on the physical world, so generating immersive, interactive and enriching experiences for the user. To take advantages of BIM and AR potential in the interaction and the intuitive management in AECO (Architecture, Engineering, Construction and Operation) projects, we propose a BIM-based AR workflow through an application called "EasyBIM". This latter allows access and interaction with a BIM model through functionalities for measurement, data consultation, collaboration, visualization and integration of information from sensors. The application is developed for mobile platforms (tablet, smartphone), and has as input an IFC file (Industry Foundation Classes). Promising test results show that the developed solution can be easily integrated into a BIM context for several use cases: marketing, collaboration, site monitoring, facility management, etc.

Influence of Transformational Leadership on Job Performance: Employee Work Engagement as Test Mediation

<p>The study aims at investigating of this study is to examine how the effect of transformational leadership on job performance by mediating employee work engagement on employees of the Human Capital and Facility Management division of PT. XL Axiata, Tbk. Jakarta. The data were collected from 58 employees Facility division of Human Capital Management PT. XL Axiata Tbk. Jakarta. For the serial analysis, SPSS macro process was utilized to test the proposed hypothesis model. Results provide evidence that follower employee work engagement mediates the relationship between perceived transformational leadership and job performance. By better understanding how leaders build engagement and drive performance in regard to a specific task, organizations can take advantage of the influence that leaders have on everyday interactions in workplace. Therefore, in this study more developed the psychological profile of effective leaders, especially the communications industry, management researchers must explore work involvement and people's orientation. </p>

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.

Enhancing Interpretability of Data-Driven Fault Detection and Diagnosis Methodology with Maintainability Rules in Smart Building Management

Data-driven fault detection and diagnosis (FDD) methods, referring to the newer generation of artificial intelligence (AI) empowered classification methods, such as data science analysis, big data, Internet of things (IoT), industry 4.0, etc., become increasingly important for facility management in the smart building design and smart city construction. While data-driven FDD methods nowadays outperform the majority of traditional FDD approaches, such as the physically based models and mathematically based models, in terms of both efficiency and accuracy, the interpretability of those methods does not grow significantly. Instead, according to the literature survey, the interpretability of the data-driven FDD methods becomes the main concern and creates barriers for those methods to be adopted in real-world industrial applications. In this study, we reviewed the existing data-driven FDD approaches for building mechanical & electrical engineering (M&E) services faults and discussed the interpretability of the modern data-driven FDD methods. Two data-driven FDD strategies integrating the expert reasoning of the faults were proposed. Lists of expert rules, knowledge of maintainability, international/local standards were concluded for various M&E services, including heating, ventilation air-conditioning (HVAC), plumbing, fire safety, electrical and elevator systems based on surveys of 110 buildings in Singapore. The surveyed results significantly enhance the interpretability of data-driven FDD methods for M&E services, potentially enhance the FDD performance in terms of accuracy and promote the data-driven FDD approaches to real-world facility management practices.