A Review of Data-Driven Accident Prevention Systems: Integrating Real-Time Safety Management in the Civil Infrastructure Context

Assadzadeh, Amin; Arashpour, Mehrdad; Rashidi, Ali; Bab–Hadiashar, Alireza; Fayezi, Sajad

doi:10.22260/isarc2019/0039

Cited by 1 publication

(3 citation statements)

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“…Different technologies such as blockchain, virtual and enhanced reality, cloud, fog, and distributed computing as well as sensing, all of them with enormous potential to be integrated into AI concert. Similarly, integrated systems using BIM platforms in combination with safety and risk monitoring and assessment are required [4], [43]- [45]. Probably these efforts and analyses make a bolder requirement to introducing Big-data into the AEC-AI industry as a cognitive platform for human development.…”

Section: Partner Technologiesmentioning

confidence: 99%

“…Then, location tracking, gait analysis, object detection, activity recognition, ergonomic assessment, etc. can be integrated into addressing risk and safety [43]. Several areas of improvement are being identified: an integrated framework combining information obtained using a variety of methods, which also may include real-time monitoring and analysis, including Big Data Engineering and Data Analytics.…”

Section: ) Ai Integrated Needsmentioning

confidence: 99%

“…Similarly, the availability of large datasets can allow for AI algorithmic training in construction safety monitoring for benchmarks. Quantitative analysis and monitoring systems for accident precursors for fall, struck-by, caught in or between, and electrocution [43].…”

Section: ) Ai Integrated Needsmentioning

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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Section: Partner Technologiesmentioning

confidence: 99%

Section: ) Ai Integrated Needsmentioning

confidence: 99%