首页|Findings from British Malaysian Institute Broaden Understanding of Structural En gineering (Systematic literature review on the application of machine learning f or the prediction of properties of different types of concrete)
Findings from British Malaysian Institute Broaden Understanding of Structural En gineering (Systematic literature review on the application of machine learning f or the prediction of properties of different types of concrete)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on structural engineering is the subject of a new report. According to news reporting out of Kuala Lumpur , Malaysia, by NewsRx editors, research stated, “Concrete, a fundamental constru ction material, stands as a significant consumer of virgin resources, including sand, gravel, crushed stone, and fresh water. It exerts an immense demand, accou nting for approximately 1.6 billion metric tons of Portland and modified Portlan d cement annually.”The news journalists obtained a quote from the research from British Malaysian I nstitute: “Moreover, addressing extreme conditions with exceptionally nonlinear behavior necessitates a laborious calibration procedure in structural analysis a nd design methodologies. These methods are also difficult to execute in practice . To reduce time and effort, ML might be a viable option. A set of keywords are designed to perform the search PubMed search engine with filters to not search t he studies below the year 2015. Furthermore, using PRISMA guidelines, studies we re selected and after screening, a total of 42 studies were summarized. The PRIS MA guidelines provide a structured framework to ensure transparency, accuracy, a nd completeness in reporting the methods and results of systematic reviews and m eta-analyses. The ability to methodically and accurately connect disparate parts of the literature is often lacking in review research. Some of the trickiest pa rts of original research include knowledge mapping, co-citation, and co-occurren ce. Using this data, we were able to determine which locations were most active in researching machine learning applications for concrete, where the most influe ntial authors were in terms of both output and citations and which articles garn ered the most citations overall.”
British Malaysian InstituteKuala Lumpu rMalaysiaAsiaCyborgsEmerging TechnologiesEngineeringMachine LearningStructural Engineeringh
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