Retrospective BIM performance analysis based on construction big data

As the Architecture, Engineering, Construction, and Facilities Management industry undergoes a profound change with Building Information Modeling (BIM), it seems the right moment to properly re-structure the inherent processes to promote a new wave of innovation. To leverage digital information from each individual project into business value for the whole industry, researchers must borrow knowledge and solutions from computational fields, such as Machine Learning, Artificial Intelligence, Data Mining and Data Science. They will provide a guide to the development, and even transformation of current BIM processes, with potential for development of new tools and automation of many tasks. What is not entirely clear is if BIM could take advantage also from Big Data Analytics, as some professionals are been advocating. In this paper, the author analyzes Big Data problems and the BIM context, and argues that BIM could not immediately take advantage from Big Datainfrastructure. Nevertheless, a route of development is suggested, which extends BIM from its predominantly building-focused models to models that encompass an entire city, which certainly will demand Big Data Analytics. Thus, a new City Information Modeling seems to be the right path of development for BIM as it turns to be integrated with Geographic Information Systems and will lead to tools that would be adequate for future Smart Cities planning and management.

PurposeThis 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.Design/methodology/approachInitially, 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.FindingsThe 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.Research limitations/implicationsThe 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.Originality/valueThe 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 presents a systematic review of Construction 4.0 in the context of the building information modeling (BIM) 4.0 premise. It comprises a review of the industry in the pre-fourth industrial revolution (4IR) age, the current and anticipated development of the 4IR, Construction 4.0’s origin and applications, and the synergy of its main drivers, i.e., the synergy of BIM with the internet of things (IoT) and big data (BD). The main aim of the paper is to determine the Construction 4.0 drivers and to what extent are they initialized by the 4IR, their development and their synergy with BIM, and the direction of BIM’s implementation in the construction phase. It was found that the main drivers of Construction 4.0, which originated from the 4IR, are BIM, IoT, and BD, but with specific implementations. The results of the analysis of BIM with IoT and/or BD revealed that the integrative approaches combining the aforementioned drivers show signs of project enhancement by providing significant benefits, such as improved real-time monitoring, data exchange and analysis, construction planning, and modeling. Furthermore, it was revealed that the main drivers are mostly applied in the project’s preconstruction phase, which is continuously developing and becoming more automated. The state-of-the-art review presented in this paper suggests that BIM is in transition, adopting Construction 4.0 to become BIM 4.0.

PurposeEffective management of highways requires management of diverse data sets including traffic volume data, roadway, and road edge and road-side data. Like all major infrastructure clients, highways administration authorities are under pressure to use such platforms for better management of data that, in addition to creating other opportunities, allows improved life cycle management of asset data and predictive analytics. This paper aims to review such opportunities and the value that can be generated through integrated life cycle data management by leveraging Big Data and building information modelling (BIM).Design/methodology/approachA literature review is initially performed to systematically gather information to identify and understand BIM as a collaborative platform. Data management applications in other industries are also reviewed. Interviews were conducted and two industry workshops were organised to understand BIM implementation challenges within highways development projects and the role BIM can play in bridging inefficiencies resulting from loss of information at handover phases. The overall understanding lead to drawing up user needs, gathering system requirements and eventually a system architecture design to promote efficient information management throughout the asset lifecycle.FindingsIt is observed that data from the design and construction phases of projects can be used to inform asset registers from an earlier stage. This information can be used to plan maintenance schedules. Moreover, it can also be integrated with data generated from numerous other sensors to develop a better picture of network operations and support key decision-making. Effective road network management involves collection and analysis of huge data from a variety of sources including sensors, mobiles, assets and Open Data. Recent growth in Big Data analytics and data integration technologies provides new opportunities to optimise operations of highways infrastructure.Research limitations/implicationsThe system architecture designed for this research is translated into a prototype system as a proof of concept. However, it needs to be tested and validated by end users to be transformed into a useful solution for the industry.Originality/valueThis paper provides an enhanced understanding of new opportunities created to optimise operations of highways infrastructure using the recent growth in Big Data analytics and data integration technologies.

The Fusion of GIS and Building Information Modeling for Big Data Analytics in Managing Development Sites

Currently, cloud computing and big data are largely applied in smart construction, or so-called industrial Internet of Things 4.0. Building Information Model (BIM) performs as an elemental information and data unit, which is extensively employed in entire construction processes (lifespan) and inter-acted with various entities including investors, employers, design institutes, construction companies, and monitors. They thus can achieve quantitative computing, measurement, and managements by sharing BIM data. The accumulation of BIM data will finally approach to a big data for construction intelligence. However, sharing BIM big data may be subtle in terms of privacy leakage, since BIM data may enclose the critical infrastructure information, homeland security data, structure parameters, and design intellectual property. It thus is of urgent importance to protect privacy in the BIM data sharing. In this paper, we propose a blockchain based method to protect data privacy in BIM data sharing, which can be applied for guaranteeing the information security in next generation industrial Internet of Things for smart construction.

PurposeIn New Zealand, building information modelling (BIM) prevalence is still in its early stages and faces many challenges. This research aims to develop a BIM adoption framework to determine the key factors affecting the success of a BIM project.Design/methodology/approachBoth primary and secondary data were employed in this research, including 21 semi-structured interviews and industry guidelines from the three most well-known global building excellence models (BEMs). The data were analysed through content analysis due to its recognised benefits as a transparent and reliable approach.FindingsLeadership, clients and other stakeholders, strategic planning, people, resources, process and results were identified as seven main categories along with 39 indicators in the BIM adoption framework. Based on the interviewees' perspectives, leadership is considered the most significant category, impacting all of the remaining categories.Practical implicationsUsing the developed framework will enhance comprehension of BIM, offering directives for those embracing BIM. This will aid construction stakeholders in being better equipped for BIM projects. Having a skilled BIM manager can lead to the success of construction projects.Originality/valueThis research contributed to the existing body of knowledge by providing the categories with specific factors that assist BIM practitioners in assessing their BIM performance for further BIM practice improvement.

As a set of new technologies to enhance life-cycle construction management processes, Building Information Modelling (BIM) has been introduced to China’s Architecture, Engineering, and Construction (AEC) industries. Adopting the framework of Technology-Organization-Environment (TOE) and the theory of innovation diffusion, this study provides an empirical analysis of BIM adoption and its impact on corporation performance, using a time series panel sample of 1109 observations of AEC enterprises listed in China’s A-share stock market. This study reveals that the decision of BIM adoption could be driven by corporate innovation strategy and government policy pressure. Overall, BIM implementation brings positive financial performance to the BIM adopted enterprises, but the differences of BIM performance between innovation strategy-led and policy-driven adoption are only significant in enterprises of state-controlled. This paper contributes to the body of knowledge in enriching the TOE framework by adding innovation strategy motivations in the organization context and government policy forces to the environment context. In addition, the findings of this paper might help policy makers around the world improve the acceptance of BIM technology and evaluate the effects of BIM policy.

Abstract. Urban and population growth results in increasing pressure on the public utilities like transport, energy, healthcare services, crime management and emergency services in the realm of smart city management. Smart management of these services increases the necessity of dealing with big data which is come from different sources with various types and formats like 3D city information, GPS, traffic, mobile, Building Information Model (BIM), environmental, social activities and IoT stream data. Therefore, an approach to mine/analysis/interpret these data and extract useful knowledge from this diverse big data sources emerges in order to extract the hidden pattern of data using computational algorithms from statistics, machine learning and information theory. However, inconsistency, duplication and repetition and misconducting with the different type of discrete and continuous data can cause erroneous decision-making. This paper focuses on providing a rules extraction and supervised-decision making methods for facilitating the fusion of BIM and 2D and 3D GIS-based information coupling with IoT stream data residing in a spatial database and 3D BIM data. The proposed methods can be used in those applications like Emergency Response, Evacuation Planning, Occupancy Mapping, and Urban Monitoring to Smart Multi-Buildings so that their input data mostly come from 2D and 3D GIS, BIM and IoT stream. This research focus on proposing the unified rules extraction and decision engine to help smart citizens and managers using BIM and GIS data to make smart decision rather than focus on applications in certain field of BIM and GIS.

The convergence of core and emerging technologies increasingly drives the digital transformation of the construction industry. This paper systematically explores the integrated application of Building Information Modeling (BIM) with technologies like Artificial Intelligence (AI), the Internet of Things (IoT), big data, and Augmented/Virtual Reality (AR/VR) in construction management, alongside the inherent challenges. Findings indicate that BIM-AI integration facilitates design optimization, error detection, and progress tracking; BIM-IoT combination enables dynamic monitoring via a “virtual model + real-time data” paradigm; big data analytics empower BIM for progress correction and risk early-warning; and AR/VR technologies significantly enhance model visualization and immersive collaboration. These integrations collectively enhance decision-making, risk control, and operational efficiency throughout the project lifecycle. However, major challenges persist, including data heterogeneity between BIM and IoT, non-standardized data hindering AI training, and technical bottlenecks in BIM-AR/VR model conversion and rendering. To overcome these barriers and harness the full potential of integrated BIM, this study proposes systematic countermeasures, including a general data fusion framework, semantic standardization, cloud-edge computing, and an intermediate conversion layer. The study further identifies the development of digital twin systems as a key future direction to achieve multi-source data closed-loop interaction. This evolution is envisioned to propel the construction industry toward unprecedented levels of intelligence and management refinement.

Operation and maintenance challenges have always been pronounced in construction projects. Many solutions have been proposed to mitigate the common problems but the solution are not sufficient to get over the challenges, therefore, many software-based concepts such as Building Information Modeling have been unveiled to evaluate the construction prior to initiating the project and envision the potential challenges which exist on the way. For higher accuracy IT solutions can be accompanied with Building Information Modeling to leverage the performance and precision of modeling such as Big Data, data mining and even RFID tags, this paper aims to carry out an in-depth literature review on how BIM alongside with Big Data solutions have parallel impacts into higher efficiency and performance of operation and maintenance in construction projects.

Purpose Big data analytics allows researchers and industry practitioners to extract hidden patterns or discover new information and knowledge from big data. Although artificial intelligence (AI) is one of the emerging big data analytics techniques, hospitality and tourism literature has shown minimal efforts to process and analyze big hospitality data through AI. Thus, this study aims to develop and compare prediction models for review helpfulness using machine learning (ML) algorithms to analyze big restaurant data. Design/methodology/approach The study analyzed 1,483,858 restaurant reviews collected from Yelp.com. After a thorough literature review, the study identified and added to the prediction model 4 attributes containing 11 key determinants of review helpfulness. Four ML algorithms, namely, multivariate linear regression, random forest, support vector machine regression and extreme gradient boosting (XGBoost), were used to find a better prediction model for customer decision-making. Findings By comparing the performance metrics, the current study found that XGBoost was the best model to predict review helpfulness among selected popular ML algorithms. Results revealed that attributes regarding a reviewer’s credibility were fundamental factors determining a review’s helpfulness. Review helpfulness even valued credibility over ratings or linguistic contents such as sentiment and subjectivity. Practical implications The current study helps restaurant operators to attract customers by predicting review helpfulness through ML-based predictive modeling and presenting potential helpful reviews based on critical attributes including review, reviewer, restaurant and linguistic content. Using AI, online review platforms and restaurant websites can enhance customers’ attitude and purchase decision-making by reducing information overload and search cost and highlighting the most crucial review helpfulness features and user-friendly automated search results. Originality/value To the best of the authors’ knowledge, the current study is the first to develop a prediction model of review helpfulness and reveal essential factors for helpful reviews. Furthermore, the study presents a state-of-the-art ML model that surpasses the conventional models’ prediction accuracy. The findings will improve practitioners’ marketing strategies by focusing on factors that influence customers’ decision-making.

Purpose Despite extensive research on building information modelling (BIM) project collaboration, prior studies have focused on traditional performance metrics, qualitative methods and formal mechanisms, leaving a gap in understanding how relational attitudes drive collaborative innovation performance. This paper aims to explore how relational attitudes (RAs) exert impacts on collaborative innovation performance (CIP) in Chinese BIM project teams. Based on the relational exchange and information processing theories, a moderated mediating model is proposed to explore the mediating role of knowledge spillover (KS) and the moderation of cognitive diversity (CD). Design/methodology/approach To gather data, this study conducted a questionnaire-based survey and collected 295 valid responses. The data were analysed using partial least squares structural equation modelling to test the proposed hypotheses. Findings Notably, RAs positively influence CIP. Furthermore, KS performs a significant mediating role between RAs and CIP. Additionally, the indirect effect of RAs on CIP via KS is moderated by CD, highlighting the role of team diversity in enhancing knowledge exchange and innovation in BIM projects. Originality/value Unlike previous studies that focus primarily on traditional project performance metrics, this paper concentrates on the impact of RAs on CIP from the perspective of relational exchange theory. Moreover, it significantly expands the concept of KS within the context of intelligent buildings, positioning BIM as a virtual technology platform for collaboration and innovation. The study also discusses the influence of inter-organisational team RAs and their determining factors on CIP through BIM application, based on the role of CD.

In today’s Business world, big data projects play an important role in increasing the profit of the business. Success of the big data project lies on the complete understanding of the customer requirement. A big data project not only gives the solution for day to day functionality of the system but also gives a solution to improve the business. Hence understanding the requirement is very important. Most of the big data projects are based on the object oriented approach. Most of the software industry relies on UML use case model to specify, analyze and design the big data project. The difference between other project and big data project is data characteristics. The three V characters, volume, velocity and variety play major role in the design decision of big data project. The existing UML use case diagram does not have a facility to represent the big data V characteristics. In this paper we are proposing big data use case diagram, suitable for specifying big data characteristics. The research result shows that big data use case diagram improves the understandability for the requirements of the big data project.

As China's urbanization process enters a new stage, urban renewal has become a key pathway for promoting sustainable urban development and improving the quality of the built environment. The green transformation of existing buildings, as a core component of urban renewal, faces prominent challenges such as low construction efficiency, high disturbance to occupants, and difficulties in coordinating multiple stakeholders and complex building systems. These issues urgently require the support of intelligent construction technologies and new construction models. This paper focuses on the integration of intelligent construction technology with the green transformation of existing buildings, with the aim of constructing a systematic and operable technical framework. First, core concepts such as intelligent construction, urban renewal, and green transformation are clarified, and the research foundation is established based on sustainable development theory, systems engineering, and life-cycle thinking. Second, by analyzing the current application status of technologies such as building information modeling, the Internet of Things, and big data in urban renewal projects, the paper systematically elaborates a full-chain technical system covering digital surveying, intelligent design, construction management, and energy-saving operation and monitoring. The study highlights how intelligent technologies enhance reconstruction accuracy, reduce resource and energy consumption, shorten construction periods, and optimize building performance. Finally, the proposed technical system provides theoretical support and practical guidance for the green renovation of existing buildings driven by intelligent construction, offering important reference value for promoting green, low-carbon, and high-quality urban development.