Understanding quality of analytics trade-offs in an end-to-end machine learning-based classification system for building information modeling

Ryu, Minjung; Truong, Hong Linh; Kannala, Matti

doi:10.1186/s40537-021-00417-x

Cited by 14 publications

(4 citation statements)

References 28 publications

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“…In addition, medium-and small-sized businesses may focus mostly on primary moneymaking and feasible operations and new methods thanks to cloud computing. Oke, Farouk Kineber, Abdel-Tawab et al and Ryu et al stated that by creating an effective business model, businesses must achieve their efficacy in enhancing their company's worth over a long period [52,53]. All corporate resources, such as work and time, formerly allocated to the IT department may now be allocated to other crucial business areas.…”

Section: Related Literaturementioning

confidence: 99%

Success of Implementing Cloud Computing for Smart Development in Small Construction Projects

et al. 2023

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The objective of this study is to ascertain the essential elements that contribute to the successful implementation of cloud computing in small-scale construction projects, with the ultimate goal of promoting intelligent development in Malaysia. The construction sector is undergoing rapid transformation, and the integration of cloud computing technology can make a substantial contribution to the achievement of project objectives and the promotion of sustainable development. Nonetheless, there exists a dearth of comprehension regarding the function of cloud computing in minor construction undertakings within the Malaysian context. In order to bridge this gap, a mixed-methods approach was implemented, which encompassed a comprehensive review of the literature, interviews with experts, and a preliminary survey that involved 160 participants. Utilizing the findings of the pilot survey, the process of Exploratory Factor Analysis (EFA) was employed to discern and eliminate nonessential determinants of success. A survey utilizing primary questionnaires was conducted with a sample size of 230 participants. The subsequent analysis of 16 critical success factors was carried out through the application of Structural Equation Modeling (SEM). The findings indicate that there are four fundamental constructs that play a crucial role in the effective execution of a project. These include cost, quality, and time management (β = 0.352); planning success (β = 0.360); organizational success (β = 0.351); and communication and coordination (β = 0.299). The research results have favorable ramifications for the construction sector in Malaysia. The integration of cloud computing technology in minor construction endeavors has the potential to augment project efficacy and foster sustainable development. This study offers a roadmap for stakeholders in the construction industry to effectively utilize cloud computing technology for smart development by identifying critical success factors.

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Section: Related Literaturementioning

confidence: 99%

Success of Implementing Cloud Computing for Smart Development in Small Construction Projects

et al. 2023

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“…Processing the answers: We performed data extraction through Statistica and Data Mining. For a higher denunciation value, we have chosen a TF-IDF function that revealed the importance of individual expressions in respondents' responses [55]. We extracted the terms into concepts and decided to work with the first two concepts with the highest termination value [56].…”

Section: Number Of Answers: 134mentioning

confidence: 99%

Young leaders as implementers of neuroscience innovations in family food businesses

Moravanská

Lušňáková

Rumanko

et al. 2023

Potr. S. J. F. Sci.

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Neuroscience and its implementation in work with human resources is an important part of managerial work. It helps to understand people and the processes of motivation, learning, and adaptation to new situations and reactions to changes in human resource management. Implementing new trends in work with human resources is also very important for ensuring the sustainability of family businesses as an irreplaceable part of national economies. Their implementation is helped by the fact that many of the family food businesses are going through the process of generational change, and family business leadership is being taken over by a generation of young managers - leaders. The contribution aimed to discover how the younger generation of managers perceives neuroscience and where they see the opportunity for its application in human resources management. Our research focused on the younger generation of managers -leaders in Slovakia's small and medium-sized food family businesses. A structured controlled interview was used for qualitative data collection, which was statistically evaluated using the Text mining method. As we discovered, some new neuroscience-based practices are already gradually being applied. By focusing our research also on a different view of the implementation of neuroscience into managerial work by gender, the conclusion is that female, young managers focused on using neuroscience to improve the working environment and in the area of human leadership. Young men as managers, were more focused on the growth of employees who already work in the company to be even more efficient and better manage the learning process. It is a very positive finding that young managers of small and medium-sized food enterprises in Slovakia have already begun actively introducing innovative methods of working with human resources using neuroscience knowledge.

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“…Moreover, in [8], images extracted from the BIM software are used and categorized into three different classes (apartment building, industrial building and other types); then a classification algorithm is developed, obtaining good accuracy results using convolutional neural networks. Another end-to-end ML-based classification system is developed in [9] with the objective of automating several steps for the classification of BIM models, which have to deal with multiple types of data. Some examples of research carried out in the domain of structural building diagnosis have been supported by AI techniques.…”

Section: Overview Of Ai Applications For the Built Environmentmentioning

confidence: 99%

Machine Learning for the Improvement of Deep Renovation Building Projects Using As-Built BIM Models

Mulero-Palencia¹,

Álvarez²,

Chicote³

2021

Sustainability

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In recent years, new technologies, such as Artificial Intelligence, are emerging to improve decision making based on learning. Their use applied to the Architectural, Engineering and Construction (AEC) sector, together with the increased use of Building Information Modeling (BIM) methodology in all phases of a building’s life cycle, is opening up a wide range of opportunities in the sector. At the same time, the need to reduce CO2 emissions in cities is focusing on the energy renovation of existing buildings, thus tackling one of the main causes of these emissions. This paper shows the potentials, constraints and viable solutions of the use of Machine Learning/Artificial Intelligence approaches at the design stage of deep renovation building projects using As-Built BIM models as input to improve the decision-making process towards the uptake of energy efficiency measures. First, existing databases on buildings pathologies have been studied. Second, a Machine Learning based algorithm has been designed as a prototype diagnosis tool. It determines the critical areas to be solved through deep renovation projects by analysing BIM data according to the Industry Foundation Classes (IFC4) standard and proposing the most convenient renovation alternative (based on a catalogue of Energy Conservation Measures). Finally, the proposed diagnosis tool has been applied to a reference test building for different locations. The comparison shows how significant differences appear in the results depending on the situation of the building and the regulatory requirements to which it must be subjected.

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Understanding quality of analytics trade-offs in an end-to-end machine learning-based classification system for building information modeling

Cited by 14 publications

References 28 publications

Success of Implementing Cloud Computing for Smart Development in Small Construction Projects

Success of Implementing Cloud Computing for Smart Development in Small Construction Projects

Young leaders as implementers of neuroscience innovations in family food businesses

Machine Learning for the Improvement of Deep Renovation Building Projects Using As-Built BIM Models

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