چکیده مقاله
This systematic review of 10 studies investigates the extent to which intelligent data mining techniques can reduce uncertainty and enhance decision making in structural engineering Findings reveal strong but highly context dependent effectiveness: reported benefits range from 17 % cost reduction in seismic dam design Amini et al , 2024 to 97 % damage detection accuracy under noisy and delayed sensor signals Salehi et al , 2019 Pattern recognition methods ANNs, SVMs, decision trees, PPSVM proved particularly effective for measurement uncertainty problems in damage detection and structural health monitoring, whereas surrogate modelling, decision tree based probabilistic approaches DTUD , and design space reduction techniques showed clear value for parameter and model uncertainty in computationally expensive structural optimization tasks Data fusion and evidence theory integrations further improved robustness against conflicting sensor information However, rigorous quantitative validation remains scarce: only two studies provided specific numerical performance metrics, none reported statistical significance, confidence intervals, or uncertainty bounds on results, and nine sources were available only as abstracts, severely limiting assessment of methodological quality Effectiveness therefore depends critically on problem type, data quality, scale laboratory vs full scale , and proper technique selection, with generalizability across structural engineering applications still poorly characterized and requiring substantially more rigorous, full text empirical validation
کلیدواژهها
نویسندگان
Amirhossein Yahyavi
Msc student of business administration MBA , Faculty of Management and Finance Sciences, Department of Management, khatam University, Tehran, Iran
شیوه ارجاع
Yahyavi, Amirhossein,1404,How Intelligent Data Mining Techniques Improve Decision-Making and Reduce Uncertainty in Structural Engineering Research,The 7th international conference on artificial intelligence and its future prospects in electrical, computer, mechanical and telecommunication engineering sciences,Mashhad
ارائهشده در
مجموعه مقالات هفتمین کنفرانس بین المللی هوش مصنوعی و چشم انداز آینده آن در علوم مهندسی برق ، کامپیوتر ، مکانیک و مخابرات21 مرداد 1404 · مشهد