Use of Artificial Intelligence in a Regulated Design Environment – A Beam Design Example

Karan, Ebrahim; Safa, Mahdi; Suh, Min Jae

doi:10.1007/978-3-030-51295-8_2

Cited by 4 publications

(6 citation statements)

References 8 publications

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“…The focus is placed on two dimensions: the level of automation (Manual, Decision support, Decision selection, Supervisory control, Full automation) (Sindhgatta et al, 2020) and the extent of automation (Structured, Structured with ad hoc exceptions, Unstructured with predefined fragments, Loosely structured and Unstructured processes) (Szelagowski and Lupeikiene, 2020). According to the theorising of Raisch and Krakowski (2021) and Karan et al (2021), a bigger impact on decision-making and less on process performance are expected following increased automation. Given this, an effect on the effectiveness and efficiency of BPP and DMP can be presumed.…”

Section: Automation-augmentationmentioning

confidence: 99%

Creating AI business value through BPM capabilities

Zebec,

Indihar Štemberger

2024

BPMJ

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PurposeAlthough businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.Design/methodology/approachThe integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.FindingsThe results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.Research limitations/implicationsIn pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.Practical implicationsThe provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.Originality/valueWhile previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

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Section: Automation-augmentationmentioning

confidence: 99%

Creating AI business value through BPM capabilities

Zebec,

Indihar Štemberger

2024

BPMJ

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“…Neural Networks (NNs) which although there are some of the first implementations in AI, new paradigms as deep neuronal networks make emerge a new landscape of use and theory. Truss Structures, which in relation to AI, some optimization applications associated to design have been developed.Similarly, the design concept is present in many objectives in the AEC industry, from automated design in BIM to structural and architectural optimization design for energy efficiency [17]- [19]. Whereas, estimation is a general concept that, in the context of AEC-AI, is related to prediction and assessment.…”

Section: ) Structure In Aec-ai Knowledge Domainmentioning

confidence: 99%

“…These quantitative measurements are useful, leading to improved monitoring, safety, productivity, and quality [53]. When the case of applications of VR in the communication area of the AEC industry is made, improvements were mainly found in building inspection, facility management, safety training, construction education, and design and review [17], [18], [54]. A fifteen-year review of 3D point cloud identifies two key areas of development, 3D model reconstruction, and geometry quality inspections.…”

Section: ) Pointcloud Vr and Visionmentioning

confidence: 99%

Section: ) Special Interest Energymentioning

confidence: 99%

“…Energy efficiency and comfort probably will become very interesting areas of development, from the design phase in architecture and engineering to parameter tuning in building operations [17], [19]. New and classical algorithms will play along with the most sophisticated area of integration in conceptual design [17], [58], [59]. BIM-based design processes in structural engineering and life cycle energy domains may allow evaluation frameworks that compute the performance criteria of sustainable and energy efficient structural systems, at the same time consolidating intuitive decision workflows.…”

Section: ) Special Interest Energymentioning

confidence: 99%

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Towards an AEC-AI Industry Optimization Algorithmic Knowledge Mapping: An Adaptive Methodology for Macroscopic Conceptual Analysis

Maureira¹,

Pinto

Yepes³

et al. 2021

IEEE Access

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The Architecture, Engineering, and Construction (AEC) Industry is one of the most important productive sectors, hence also produce a high impact on the economic balances, societal stability, and global challenges in climate change. Regarding its adoption of technologies, applications and processes is also recognized by its status-quo, its slow innovation pace, and the conservative approaches. However, a new technological era -Industry 4.0 fueled by AI-is driving productive sectors in a highly pressurized global technological competition and sociopolitical landscape. In this paper, we develop an adaptive approach to mining text content in the literature research corpus related to the AEC and AI (AEC-AI) industries, in particular on its relation to technological processes and applications. We present a first stage approach to an adaptive assessment of AI algorithms, to form an integrative AI platform in the AEC industry, the AEC-AI industry 4.0. At this stage, a macroscopic adaptive method is deployed to characterize "Optimization", a key term in AEC-AI industry, using a mixed methodology incorporating machine learning and classical evaluation process. Our results show that effective use of metadata, constrained search queries, and domain knowledge allows getting a macroscopic assessment of the target concept. This allows the extraction of a high-level mapping and conceptual structure characterization of the literature corpus. The results are comparable, at this level, to classical methodologies for the literature review. In addition, our method is designed for an adaptive assessment to incorporate further stages.

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