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Energy analysis (EA) within a building information modelling (BIM) enables consistent data integration in central repositories and eases information exchange, reducing rework. However, data loss during information exchange from different BIM uses or disciplines is frequent. Therefore, a holistic approach for different BIM uses enables a coherent life cycle information flow. The life cycle information flow drives the reduction of data loss and model rework and enhances the seamless reuse of information. The latter requires a specification of the EA key performance indicators (KPIs) and integrating those in the process.

The paper presents a set of KPIs extracted from the developed EA process maps and interviews with expert stakeholders. These KPIs stem from the literature review and link to the benefits of EA through industry expert review. The study includes (1) development and validation of EA process maps adjusted to requirements from different stakeholders. (2) KPIs aligned with the EA process map, (3) identification of the drivers that can facilitate life cycle information exchange and (4) opportunities and obstacles for EA within BIM-enabled projects.

This paper depicts a viable alternative for EA process maps and KPIs in a BIM-enabled AEC design industry. The findings of this paper showcase the need for an EA within BIM with these KPIs integrated for a more effective process conforming to the current Open BIM Alliance guidance and contributing towards sustainable life cycle information flow.

The limitation of the research is the challenge of generalising the developed EA process maps; however, it can be adjusted to fit defined organisational use. The findings deduced from the developed EA process map only show KPIs to have the ability to facilitate adequate information flow during EA.

The AEC industry will benefit from the findings of this primary research as the industry will be able to contrast its process maps and KPIs to those developed in the paper.

This paper benefits the societal values in EA for the built environment in the design stages. The subsequent life cycle information flow will help achieve a consistent information set and decarbonised built environment.

The paper offers a practical overview of process maps and KPIs to embed EA into BIM, reducing the information loss and rework needed in the practice of this integration. The applicability of the solution is contrasted by consultation with experts and literature.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

Companies are increasingly implementing business process management systems (BPMSs) to support their processes. However, there is a gap in the literature regarding whether users also use BPMSs to manage the knowledge needed for processes to be completed. This study aims to analyze the factors that cause users to use BPMSs to manage the knowledge required in business processes.

The paper proposes an original model that integrates two successful information system models applied to BPMSs and knowledge management systems. To test the hypotheses derived from this new model, data were collected from 242 mature BPMS users from 12 Spanish and Latin American companies. Structural equation modeling with AMOS was used to examine the model.

Users’ perceived usefulness of a BPMS when using it for knowledge management (KM) is the only factor influencing them to use it for KM.

This study has practical implications for managers wishing to successfully implement a BPMS to support processes and for employees to use the knowledge embedded in the tool. The latter will only happen if users perceive the tool’s usefulness for KM.

To the best of the authors’ knowledge, this model is the first empirically validated model to successfully analyze BPMS users’ tendency to use BPMSs as a tool to support necessary KM in processes.

Although data rich building information models have been widely adopted in the Architecture Engineering and Construction (AEC) industry in the United Kingdom (UK), use of 2D drawings on site for construction is still the norm. The ability of 2-dimensional (2D) drawings to convey complex 3-dimensional information is limited and requires interpretation from operatives, and 2D drawings can be quickly superseded by model updates. Although constructing directly from a model has been adopted in the aerospace and automotive industries, its use in construction is in its infancy. This research therefore aims to investigate the potential for, and barriers to, model-based construction in the UK.

This research uses a qualitative approach, thematically analysing 13 semi-structured interviews with UK-based construction professionals who have experience of paperless or model-based construction.

Although model -based construction has been implemented to a limited extent on some civil engineering projects; research and investment in software, network capacity, legal and contractual issues, and cultural and human factors will need to be considered before model-based construction can be implemented more widely.

The research contributes to an understudied, emergent area of construction practice and outlines hurdles that need to be understood and overcome before more widespread adoption of model-based construction can take place.

Digital Libraries are complex, and this complexity is a motive to study user success on the behalf of appropriate user success models. These models comprise the factors which play a part between people, technology and organizations. The purpose of this study was to specify and examine an integrated digital library user success (IDLUS) model within the context of digital library settings, Higher Education Commission National Digital Library (HEC-NDL) of Pakistan, by adopting and reusing the existing digital library and Web success models.

Stratified random sampling technique was used to choose the sample from the University of the Punjab, a highly ranked public sector university in Pakistan. Participants were asked to complete an adapted survey questionnaire. A total of 355 completed and usable questionnaires were obtained. Data analyses through confirmatory factor analyses and structural equation modeling produced the results that have supported the proposed IDLUS model. The proposed IDLUS model was tested and supported through model fit statistics in the academic computing environment of the HEC-NDL of Pakistan.

Findings revealed that relationships between the latent variables hypothesized in the model were confirmed.

The study has both theoretical and practical ramifications for academicians and information system designers and developers.

The IDLUS model is recommended first time in the history of librarianship in Pakistan as an overall user success model in the digital library information system computing environment. That made numerous recommendations for future research in the field of information management, particularly for digital library development at national and international levels.

The possibility of integrating building information in an augmented reality (AR) environment provides an effective solution to all phases of a building's lifecycle. This paper explores the integration of building information modelling (BIM) and AR to effectively visualise building information models in an AR environment and evaluates the currently available AR tools.

A BIM model of a selected office room was created and superimposed to the actual physical space using two different AR devices and four different AR applications. The superimposing techniques, accuracy and the level of information that can be visualised were then investigated by performing a walk-through analysis.

From the investigation, it can be concluded that model positioning can be inaccurate depending on the superimposing method used and the AR device. Moreover, using the currently available techniques, only static building information can be superimposed and visualised in AR, showing a need to integrate data from Internet of Things (IoT) sensors into the current BIM-AR processes to allow visualisation of accurate and high-quality operational building information.

A practical process and method for visualising and superimposing BIM models in an AR environment have been described. Recommendations to improve superimposing accuracy are provided. The assessment of type, quality and level of detail that can be visualised indicates the areas that need improvement to increase the effectiveness of building information's visualisation in AR.

This study aims to leverage the implementation of building information modelling (BIM) in facilities management (FM), and thus the integration of BIM and FM are explored. It looks at identifying the information needed to implement BIM application in FM, to determine the level of implementation of BIMFM as well as to recommend BIMFM application mapping for building life cycle.

This study applied qualitative methodology, upon which a purposive sampling to 16 interviewees were conducted among professionals involved in any BIM, FM, BIMFM projects. Final analysis was conducted using thematical analysis by ATLAS.ti software.

The types of information required to implement BIMFM range from managerial information, commercial information, technical information and all full life cycle data. The implementation of BIMFM is agreed as beneficial, as it builds up efficiency of building performance, able to close gap of loses information, helps to improve FM in terms of data management, mitigates the problems, identifies priority defects and to enhance the FM processes. Respondents also believed that the causes where there is no implementation of BIMFM is in the situations where things are in order and when it becomes very expensive.

This paper has managed to gather the essential elements toward leveraging the implementation of BIMFM in digital construction project which are in the means of information types, the needs of BIMFM implementation and towards the end drawing the BIMFM implementation plan framework that could be used as a reference for the practitioners and industry.

Textual information about restaurants, such as online reviews and food categories, is essential for consumer purchase decisions. However, previous restaurant recommendation studies have failed to use textual information containing essential information for predicting consumer preferences effectively. This study aims to propose a novel restaurant recommendation model to effectively estimate the assessment behaviors of consumers for multiple restaurant attributes.

The authors collected 1,206,587 reviews from 25,369 consumers of 46,613 restaurants from Yelp.com. Using these data, the authors generated a consumer preference vector by combining consumer identity and online consumer reviews. Thereafter, the authors combined the restaurant identity and food categories to generate a restaurant information vector. Finally, the nonlinear interaction between the consumer preference and restaurant information vectors was learned by considering the restaurant attribute vector.

This study found that the proposed recommendation model exhibited excellent performance compared with state-of-the-art models, suggesting that combining various textual information on consumers and restaurants is a fundamental factor in determining consumer preference predictions.

To the best of the authors’ knowledge, this is the first study to develop a personalized restaurant recommendation model using textual information from real-world online restaurant platforms. This study also presents deep learning mechanisms that outperform the recommendation performance of state-of-the-art models. The results of this study can reduce the cost of exploring consumers and support effective purchasing decisions.

关于餐厅的文本信息, 如在线评论和食品分类, 对于消费者的购买决策产生至关重要。然而, 先前的餐厅推荐研究未能有效利这些文本信息去预测消费者喜好。本研究提出了一种新颖的餐厅推荐模型, 以有效估计消费者对多个餐厅属性的评估行为。

我们从 Yelp.com 收集了来自25,369名消费者对 46,613 家餐厅的 1,206,587 条评论。利用这些数据, 我们通过结合消费者身份和在线消费者评论生成了消费者偏好向量。然后, 我们结合了餐厅身份和食品分类来生成餐厅信息向量。最后, 考虑到餐厅属性向量, 本研究调查了消费者偏好和餐厅信息向量之间的非线性交互关系。

我们发现, 所提出的推荐模型相比于之前最先进的模型表现出更优秀的性能, 这表明结合消费者和餐厅的各种文本信息是预测消费者喜好的基本因素。

据我们所知, 这是第一项利用来自真实在线餐厅平台的文本信息开发个性化餐厅推荐模型的研究。本研究还提出了胜过最先进模型的深度学习机制。本研究的结果可以降低探索消费者行为的成本并支持有效的购买决策。

This research has been conducted with a view to creating a framework to integrate risk management based on building information modeling (BIM) information.

In this research, all the information related to the construction of a residential project including 3D, 4D and 5D BIM models and the execution and control phases information was collected, and the risk list was determined for each activity accordingly.

The present study has suggested a framework for risk management in order to optimize project changes.

The lack of integration between 3D, 4D and 5D modeling besides execution information is a fundamental problem in many projects. The gap between these two groups of information will lead to improper management and late decisions, eventually imposing unforeseen delays and cost overruns. Risk management by the means of adopting a new approach has been addressed in recent studies using new methods, such as BIM and its associated technologies, some of which were mentioned in the review of theoretical literature in this research.