Search results

This paper aims to describe how building information model (BIM) and big data can be combined in the same interface for providing new value to stakeholders, such as the property owner and user, as well as property service and workplace service companies. The research presents a new concept, which shows how the BIM can be exploited efficiently during maintenance.

Initially, existing facility management (FM) processes were investigated to find out how to digitize them and identify bottlenecks. Second, BIM’s data content was explored to identify the information that could be used to streamline FM processes. Third, the potential of the active data measured in the building was evaluated. Finally, research was undertaken to find out how constantly fluctuating information can be combined with BIM objects and what kind of added value that combination could offer. The literature review was used to support the primary contribution. In addition, the research problems were described and the basics of the research were obtained by interviews. The author has interviewed 27 professionals from several stakeholders.

The first finding is that the BIM can serve as a platform for building use, various services and management when it has been adequately generated during the planning and construction phases and enriched before being commissioned. The other essential finding is the theory of conditions data model (CDM), which is a technical environment that combines active data with BIM. The most important advantages of BIM in FM are as follows: • Building owner attains better user satisfaction, acquires better quality and smarter services, saves energy, ensures better indoor conditions and improves building profitability. • Service providers can develop and offer new services, speed up operations, save resources and generate more profits. • The occupant gets a better user experience, faster and higher quality services and better indoor conditions.

The CDM enables to generate for the real estate and construction (RE&C) sector a novel BIM-based ecosystem with standard rules, instead of every individual operator developing his/her own unique solution for BIM use in FM. This will have an impact on the entire RE&C sector’s operating methods and will have significant financial implications in the near future. Application of this research is limited to office buildings where indoor condition measuring is undertaken continuously and where the knowledge of the use cases of spaces is available. In addition, the proper BIM in the Industry Foundation Classes format must exist. The evaluation of the validity of big data is not discussed in this article. Visualization of data and content of user interfaces will be the topic of another article by the author. This article does not deal with intricate technical details, but crucial issues are defined.

The article presents a unique method for BIM use in FM. The theory of CDM (how to combine active data with BIM) is completely new and a similar solution has not been presented earlier. The theory of the presented method will be the crucial key for BIM use and will lead worldwide commissioning. Currently, the theory is under test in the practical pilot project. The results of the project will be published in the next article.

This paper aims to discuss and bring to the attention of researchers and practitioners the data management issues relating to condition‐based maintenance (CBM) optimization.

The common data quality problems encountered in CBM decision analyses are investigated with a view to suggesting methods to resolve these problems. In particular, the approaches for handling missing data in the decision analysis are reviewed.

This paper proposes a data structure for managing the asset‐related maintenance data that support CBM decision analysis. It also presents a procedure for data‐driven CBM optimization comprising the steps of data preparation, model construction and validation, decision‐making, and sensitivity analysis.

Analysis of condition monitoring data using the proportional hazards modeling (PHM) approach has been proved to be successful in optimizing CBM decisions relating to motor transmission equipment, power transformers and manufacturing processes. However, on many occasions, asset managers still make sub‐optimal decisions because of data quality problems. Thus, mathematical models by themselves do not guarantee that correct decisions will be made if the raw data do not have the required quality. This paper examines the significant issues of data management in CBM decision analysis. In particular, the requirements of data captured from two common condition monitoring techniques – namely vibration monitoring and oil analysis – are discussed.

This paper offers advice to asset managers on ways to avoid capturing poor data and the procedure for manipulating imperfect data, so that they can assess equipment conditions and predict failures more accurately. This way, the useful life of physical assets can be extended and the related maintenance costs minimized. It also proposes a research agenda on CBM optimization and associated data management issues.

This paper aims to present approaches towards improving some specific infrastructure maintenance principles, strategies, models and practices, based on a recent study of bridge management systems in Hong Kong. A specific goal is to develop better informed and more systematic approaches to condition assessment, deterioration forecasting, and maintenance decision making over the life‐cycle of the built asset.

Improved performance prediction and decision‐making approaches are developed and presented based on a research exercise to formulate a maintenance management framework for concrete bridge elements in Hong Kong. This includes for example, the presentation of decision‐making approaches for optimizing inspection intervals on bridge expansion joints.

The findings show that judicious integration is needed in incorporating valuable elements of, and lessons learned from, previous practice with proposed new strategies/ principles, models and practices for specific scenarios.

Based on the findings, practitioners' understandings can be deepened as regards the barriers to improving condition assessment, deterioration forecasting, and maintenance decision making over the life‐cycle of the built asset. Furthermore, the results also provide useful information for developing strategies and practices to improve currently used infrastructure management systems.

Major obstacles are overcome in developing better informed and more systematic approaches as above, and in extending current knowledge on condition assessment, performance prediction and decision‐making models by utilizing more pertinent data and addressing some barriers in practical implementation.

The purpose of this paper is to evaluate six different graphical user interfaces (GUIs) for facilities operations using human–machine interaction (HMI) theories.

The authors used a combined multi-functional method that includes a review of the theories behind HMI for GUIs as its first approach. Consequently, heuristic evaluations were conducted to identify usability problems in a professional context. Ultimately, thematic interviews were conducted with property managers and service staff to determine special needs for the interaction of humans and the built environment.

The heuristic evaluation revealed that not all the studied applications were complete when the study was done. The significant non-motivational factor was slowness, and a lighter application means the GUI is more comfortable and faster to use. The evaluators recommended not using actions that deviate from regular practice. Proper implementation of the GUI would make it easier and quicker to work on property maintenance and management. The thematic interviews concluded that the GUIs form an excellent solution that enables communication between the occupant, owner and service provider. Indoor conditions monitoring was seen as the most compelling use case for GUIs. Two-dimensional (2D) layouts are more demonstrative and faster than three-dimensional (3D) layouts for monitoring purposes.

The study provides an objective view of the strengths and weaknesses of specific types of GUI. So, it can help to select a suitable GUI for a particular environment. The 3D view is not seen as necessary for monitoring indoor conditions room by room or sending a service request. Many occupants’ services can be implemented without any particular layout. On the other hand, some advanced services were desired for the occupants, such as monitoring occupancy, making space reservations and people tracking. These aspects require a 2D layout at least. The building information model is seen as useful, especially when monitoring complex technical systems.

Earlier investigations have primarily concentrated on investigating human–computer interaction. The authors’ studied human–building interaction instead. The notable difference to previous efforts is that the authors considered the GUI as a medium with which to communicate with the built environment, and looked at its benefits for top-level processes, not for the user interface itself.

One of the most critical infrastructures is a data center (DC) because of it having many servers, computers and other equipment. DCs provide online services for various companies in the information technology (IT) industry. DC facilities should provide reliable online services while addressing the required quality and performance level considering maximum reliability and availability. The purpose of this study is to represent and classify the main findings in this area and to identify the main research gaps and shortcomings from the perspective of research.

This paper provides an organized and systematic literature review focusing on topics regarding the operation and maintenance (O&M) management of DCs.

Although there are several studies on O&M management systems for industrial systems and facilities, a limited number of studies with few methods and models have focused on DCs so far and these facilities require more attention. This paper identifies the issues and challenges for DC buildings and facilities and provides a conclusion of the findings to highlight the main research limitations for discovering new potential methods as future research opportunities.

The paper has highlighted the main practical issues of DCs in terms of maintenance management. Several research works have been discussed specifically for DC’s maintenance, which makes this paper a credible source for researchers, maintenance managers and companies involved in the area of DC. Because several of the reviewed literature were based on real case studies, decision-makers in the DC maintenance sector can take advantage of new research on maintenance scheduling to reduce the costs of maintenance.

The paper has presented a comprehensive list of frequent keywords in recent publications related to O&M management for DCs. It has provided a categorized list of publications based on by their topic, methodology and case study. Because this paper has discussed research works specifically for DC’s maintenance, it is a credible source for researchers, maintenance managers and companies involved in the area of DCs.

The massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance actions lead to rapid deterioration, decreased service life, lower level of service and increased community disruption. Therefore, this paper aims at providing a state-of-the-art review of the literature with respect to deep learning techniques for detecting distress in both pavements and buildings; research advancements per asset/structure type; and future recommendations in deep learning applications for distress detection.

A critical analysis was conducted on 181 papers of deep learning-based cracks detection. A structured analysis was adopted so that major articles were analyzed according to their focus of study, used methods, findings and limitations.

The utilization of deep learning to detect pavement cracks is advanced compared to assess and evaluate the structural health of buildings. There is a need for studies that compare different convolutional neural network models to foster the development of an integrated solution that considers the data collection method. Further research is required to examine the setup, implementation and running costs, frequency of capturing data and deep learning tool. In conclusion, the future of applying deep learning algorithms in lieu of manual inspection for detecting distresses has shown promising results.

The availability of previous research and the required improvements in the proposed computational tools and models (e.g. artificial intelligence, deep learning, etc.) are triggering researchers and practitioners to enhance the distresses’ inspection process and make better use of their limited resources.

A critical and structured analysis of deep learning-based crack detection for pavement and buildings is conducted for the first time to enable novice researchers to highlight the knowledge gap in each article, as well as building a knowledge base from the findings of other research to support developing future workable solutions.

The aim of this paper is to engage with the translation and linking of the “scientific knowledge” of theory on local economic growth with the “practical knowledge” of, on the one hand, local economic policy formulation and, on the other hand, entrepreneurship and entrepreneurship education. The paper uses theoretically informed empirical modelling to identify and prioritise the drivers of local economic growth using data for Australia. The analyses demonstrate the significance of human capital and an enterprise culture in promoting local employment growth. From these results it is suggested that “bottom up” entrepreneurial education and related, but more “top down”, enterprise facilitation are practical mechanisms for achieving such local growth. These results suggest the great importance of translating “scientific knowledge” into “practical knowledge” to allow communities to engage with the knowledge economy.

This paper aims to Test the capabilities/accuracies of four deep learning pre trained convolutional neural network (CNN) models to detect and classify types of highway cracks, as well as developing a new CNN model to maximize the accuracy at different learning rates.

A sample of 4,663 images of highway cracks were collected and classified into three categories of cracks, namely, “vertical cracks,” “horizontal and vertical cracks” and “diagonal cracks,” subsequently, using “Matlab” to classify the sample to training (70%) and testing (30%) to apply the four deep learning CNN models and compute their accuracies. After that, developing a new deep learning CNN model to maximize the accuracy of detecting and classifying highway cracks and testing the accuracy using three optimization algorithms at different learning rates.

The accuracies result of the four deep learning pre-trained models are above the averages between top-1 and top-5 and the accuracy of classifying and detecting the samples exceeded the top-5 accuracy for the pre-trained AlexNet model around 3% and by 0.2% for the GoogleNet model. The accurate model here is the GoogleNet model as the accuracy is 89.08% and it is higher than AlexNet by 1.26%. While the computed accuracy for the new created deep learning CNN model exceeded all pre-trained models by achieving 97.62% at a learning rate of 0.001 using Adam’s optimization algorithm.

The created deep learning CNN model will enable users (e.g. highway agencies) to scan a long highway and detect types of cracks accurately in a very short time compared to traditional approaches.

A new deep learning CNN-based highway cracks detection was developed based on testing four pre-trained CNN models and analyze the capabilities of each model to maximize the accuracy of the proposed CNN.

The paper aims to analyze the relationships between ethical leadership (EL), job satisfaction (JS), affective commitment (AC) and turnover intention (TI) that might make accountants quit withdrawal and become productive and useful in private organizations operating in Istanbul.

Data were collected through an online survey using a simple random sampling methodology, obtained from 153 accountants working in companies in Istanbul. The methodology included descriptive statistics, factor analysis, structural equation modeling and mediation analysis.

Concerning direct relationships between EL, JS, AC and TI, EL has significant positive associations with JS and AC, whereas EL has a significant negative association with TI. JS has a significant positive association with AC, whereas JS has a significant negative association with TI. Also, AC has a weak significant negative association with TI. Given indirect relationships among EL, JS, AC and TI, JS and AC mediate the relationship between EL and TI. Finally, a similarity is found when comparing Generation X and Generation Y in terms of overall JS, AC and TI.

The study is limited solely to companies functioning in Istanbul and incorporates a low number of respondents. Therefore, the results cannot be considered to be accurate for the whole country. The study might guide both private and public organizations in which owners/managers develop strategic plans.

The study fills the gap in research on organizational behavior where little has existed until now that probes the EL–JS–AC–TI links in Turkey. A few studies measure the TIs of accountants. Furthermore, EL and AC are rarely evaluated in the field of accounting in Turkey.

This paper aims to explore the opportunities of integrating internet of things (IoT) with building information modelling (BIM) to support the digital transformation of facilities management (FM). In this regard, a conceptual framework is proposed to implement IoT-BIM-based smart FM in buildings.

A semi-systematic literature review was conducted to examine the opportunities of integrating IoT-BIM-based smart FM.

BIM models are seldom used during the operations stage, and the comprehensive digital information developed during the design and construction stage is not efficiently used throughout the building’s life cycle. Therefore, this paper suggests that IoT-BIM can be effectively integrated into six FM functions, namely, energy management, operations and maintenance management, space management, FM project management, emergency management and quality management. IoT-BIM provides a beneficial platform for the digital transformation in FM, optimising the effectiveness and efficiency of buildings.

As a recent approach, the integration of BIM with IoT has created a new direction for moving from traditional FM to digitalise smart FM. However, the adaptation of IoT-BIM concept, particularly for FM, is yet to be explored. Hence, this paper contributes to the IoT-BIM research in the FM domain by highlighting six IoT-BIM-based smart FM for digital transformation in FM.