BIM based digitalization for infrastructure asset management in Indian construction industry: A Review

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by Rhijul Sood, Laishram Boeing Singh | IIT Guwahati
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Infrastructure sector is a backbone of economic development for most of the countries worldwide. But, maintenance of existing as well as newly built infrastructure projects is falling behind even after heavy expenditures.

The present Asset Management (AM) procedures result in over expenditures due to various factors which have been addressed by various authors. The paper has summarized different categorizations of factors which causes barriers for adoption of digital technology such as Building Information Modelling (BIM) particularly in India construction industry scenario.

The COVID-19 pandemic has made all the people related to construction industry, particularly the primary stakeholders realize about shifting the focus on digital technology including BIM during all the life cycle phases of an infrastructure asset.

This paper, therefore, aims to answer systematically about impact of Covid-19 on global construction industry, BIM technology, asset management using BIM, Indian construction industry and the challenges it faces in adopting BIM during various lifecycle stages.

The research listed 35 peer-reviewed articles from three databases which demonstrate the status of BIM implementation in India for AM and the detailed factors which are responsible for low level of maturity of BIM in Indian construction sites.

The paper also discuss about the Indian government policies related to Make In India, Digital India and National Infrastructure Pipeline (NIP) to make construction sector more advanced, competitive and developed through such initiatives.

Machine Learning for the Digitization of Buildings and Bridges

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by Markus König | Ruhr University,Germany
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Digital models are not available for many buildings and infrastructures that have been built before the wide adoption of Building Information Modeling for design and planning. However, digital models offer many advantages for operation and maintenance, including optimization of energy use, reduction in costs, and longer serviceability of the built environment.

Nowadays, many essential data about buildings or infrastructures are already collected through 2D drawings, documents, point clouds and images. The data is either available from the planning and construction phase or can be newly generated by technological devices like drones, laser scanners, and robots. However, these data have high variability and complexity, especially due to different collection technologies and the application of different non-standardized data structures.

Machine Learning (ML) provides many possibilities to process information from various data types and learn patterns necessary for creating digital models. However, from the research perspective, the use of ML is not always straightforward as the access to and availability of large data is not readily given. If data is available, the complexity of building data poses challenges for ML methods to learn the required patterns.

With the minisymposium, we would like to host conference papers that contribute to the following topics:
• Creating geometry of buildings, e.g. using images or point clouds
• Extraction of semantical information, e.g. using drawings and documents
• Data acquisition and processing, and labelling techniques
• Generation and application of synthetic data
• Data post-processing and model generation

Digital Twin Construction

by Rafael Sacks, Jhonattan G. Martinez, and Timson Yeung
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Virtual Construction Laboratory (VClab) | National Building Research Institute | Technion–Israel Institute of Technology
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With the growing maturity of site and supply chain monitoring technologies for the construction, the opportunity arises for the sector to catapult to more efficient production, business models, and value chains rooted in concepts associated with the Industry 4.0 paradigm [1]. This new paradigm for production control in construction is called ‘Digital Twin Construction’ (DTC) [2]. Integration of lean construction principles of planning, control, and production flow with monitoring technologies and artificially intelligent software modules to infer project status information is key to the DTC paradigm.

This mini-symposium aims to attract papers presenting new findings in the field of building information modeling and digital twin technology; simulation and process modeling; big data, sensing, and machine learning; built environment monitoring; control, analysis, and design; Information and communication technologies; project design, construction, planning, and management; reality capture technologies and robotics, automation, and control. The topics to be covered, but not limited to, are:

• Methods for data acquisition on site
• Merging and interpreting monitoring data to provide helpful project status information (PSI)
• Algorithms for processing raw monitored data
• Comparison of intent information (project design and process plan) with status information (as-built and as-performed information) to formulate actionable knowledge
• Simulation, prediction, and optimization of expected project performance
• Delivering project intent information to actors in a construction project

Amongst others, we specifically call on researchers engaged in current EU research projects on the theme of Digital Building Twins (Ashvin, BIMprove, BIM2TWIN, COGITO) to participate and share their findings.

Advancing construction safety, health, and well-being: Building new digital processes

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by Jochen Teizer | Department of Civil and Architectural Engineering, Aarhus University, Denmark
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This abstract proposes a minsymposia within ICCCBE 2022 for the topic of Advancing Construction Safety, Health, and Well-being: Buiding a New Digital World. Well respected researchers will be invited to submit their paper contributions on this topic as it relates to computing in civil and building engineering.
The last few years have seen many significant advances in construction safety practices and thinking. Much has happened as a result of the increased focus on construction safety research, technological innovation and the broader safety engineering and management evolution toward managing risk. This minisymposia gives a novel opportunity to catch up on the state-of-the-art [1] and highlight future avenues that advance this field
[2]. The minisymposia will be organized by experienced researchers in the construction safety, health, and well-being domain. Among them is Jochen Teizer, Professor from Aarhus University, Denmark. It will attract both researcher and industry leaders, incl. domain experts from South Africa.

Participants will benefit from this minisymposia by learning about:

• The value of conducting risk assessment and Prevention through Design/Planning (PtD/P) in the construction industry, e.g., utilizing digital processes involving rule and code compliance checking.
• Recognizing the vulnerabilities of today’s construction workforce and what interventions are most effective, e.g., adapting wearables for monitoring/alerting, IoT for data communication and analysis.
• New technology that can better enable identification of exposures in construction worksites and provide knowledge for safety operations decision making, e.g., benefiting from digital twins for occupational safety, health, and well-being in construction.

Participants will leave this minisymposia with the ability to:

• Prepare for construction industry safety, health, and well-being challenges now and in the future
• Better manage and respond to risk ahead of and during the construction phase
• Leverage technology to make better use of time and resources (providing additional benefits)
• Assess the characteristics of an organization safety culture to identify strengths and weaknesses upon implementation of computational and digital methods for safety, health, and well-being
• Build a better (safer and healthier) workforce by providing personalized feedback
• Introduce a virtual learning platforms as effective tools for safety and health education and training
• Align safety, health and well-being measures for technology as a positive impact on business operations

Space Partitions: An alternative to domain assembly in geometric modeling

by W. Huhnt, P. J. Pahl, C. Clemen, V. Galishnikova, G. Suter, T. Hartmann
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Technical University of Berlin | HTW Dresden – University of Applied Sciences | People’s Friendship University of Russia |  TU Wien, Austria
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Geometric modeling is state of the art in all engineering disciplines. The geometric description of spaces, components and rooms, which are referred to as domains, is fundamental for a wide range of applications in Building Information Modeling. The traditional focus in geometry is on the shape of domains. Domains are constructed step by step and assembled to form a model. Topological neighborhood relations between domains are usually regarded as a helpful additional feature.

Space partitioning changes the focus on geometry in a fundamental way. Topology becomes the center of attention. Topological data structures store topology explicitly. The benefits are obvious: the extent of geometrical calculations with their well-known problems of topological inconsistencies is dramatically reduced, and they frequently become unnecessary.

Space partitioning is not new, but its potential has not yet been investigated in the required detail to the best knowledge of the authors of this proposal. The intended special session presents research on space partitions with a special focus on applications in architecture and civil engineering.

The following list of examples, which will be used to illustrate the power
of space partitions, is not complete:

• Multiply-connected and non-convex domains are state of the art in architecture and civil engineering. However, traditional approaches subdivide these domains into sets of simply-connected and convex objects. Thus the number of objects increases. This influences the storage requirements and the runtime behavior. Space partitions can handle multiply-connected and non-convex domains without subdivision.
• Topological data structures are not readily understandable by users due to their complexity. On the other hand, efficient algorithms require such data structures. The challenge is to hide topological
  data structures from the user. In a space partitioning approach, a suitable separation of the topological and the geometric models can lead to efficient topological data structures, which are completely hidden from the user.
• Point clouds are state of the art as origin for the measurement of the geometry of buildings. Existing approaches identify surfaces. They do not make use of the fundamental information that there is an empty space from the laser to all detected surfaces. This information can be considered in a space partitioning approach to improve the methods of detection of surfaces of buildings.
• Existing boundary representations of domains can be transformed into a space partition. This permits the identification of clashes and voids in a single step. Research results can illustrate the benefits.