نشریه مهندسی عمران امیرکبیر

نشریه مهندسی عمران امیرکبیر

طبقه‌بندی آسیب در نمونه‌های ملات‌سیمانی حفره‌دار با استفاده از الگوریتم‌های یادگیری ماشین

نوع مقاله : مقاله پژوهشی

نویسندگان
دانشکده مهندسی معدن، دانشگاه صنعتی امیرکبیر، تهران، ایران
چکیده
این پژوهش به بررسی کارایی روش غیرمخرب فراصوتی در شناسایی و طبقه‌بندی آسیب در نمونه‌های ملات سیمانی با طرح‌های اختلاط مختلف می‌پردازد. بدین منظور، مجموعه‌ای از نمونه‌ها با مقادیر متفاوت محتوای سیمان (CC) تهیه و در دو وضعیت سالم و آسیب‌دیده مورد آزمایش قرار گرفتند. داده‌های حاصل از سیگنال‌های فراصوتی، پس از پیش‌پردازش و استخراج ویژگی‌های آماری و زمانی–فرکانسی، به‌عنوان ورودی به سه الگوریتم یادگیری ماشین شامل K نزدیک‌ترین همسایگان (KNN)، بیز ساده (Naïve Bayes) و جنگل تصادفی (Random Forest) اعمال شدند. نتایج تحلیل نشان داد که الگوریتم بیز ساده به‌دلیل توانایی در مدیریت ویژگی‌های مستقل و غیرهمبسته داده‌های فراصوتی، در مقایسه با سایر روش‌ها عملکرد دقیق‌تری دارد و با دقت 99.00 ± 3.16 درصد و یادآوری 97.50 ± 7.91 درصد بالاترین صحت طبقه‌بندی را ارائه کرد. نوآوری اصلی این تحقیق در ترکیب رویکردهای یادگیری ماشین با روش‌های غیرمخرب برای تحلیل تغییرات انرژی سیگنال‌های فراصوتی و تشخیص زودهنگام آسیب در ملات‌های سیمانی با نسبت‌های مختلف محتوای سیمان است. این چارچوب تحلیلی داده‌محور و تکرارپذیر، ضمن افزایش دقت و اطمینان در شناسایی آسیب، قابلیت توسعه برای پایش سلامت و ارزیابی مداوم سازه‌های بتنی در مقیاس‌های عملی را دارد و می‌تواند به‌عنوان ابزاری کارآمد در ارتقای دوام و ایمنی زیرساخت‌های عمرانی مورد استفاده قرار گیرد.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Damage Classification in Hollow Cement Mortar Specimens Using Machine Learning Algorithms

نویسندگان English

abolfazl faghihi
hamed mola davoodi
Mining Engineering department; Amirkabir University
چکیده English

This study investigates the effectiveness of non-destructive ultrasonic testing for detecting and classifying damage in cement mortar specimens with different mix designs. To this end, a set of specimens with varying cement content (CC) were prepared and tested in both undamaged and damaged states. Ultrasonic signal data, after preprocessing and the extraction of statistical and time–frequency features, were used as inputs to three machine-learning algorithms: K-Nearest Neighbors (KNN), Naïve Bayes, and Random Forest. Analysis results indicated that the Naïve Bayes classifier, owing to its ability to handle independent and uncorrelated features of the ultrasonic data, outperformed the other methods and yielded the highest classification performance, achieving an accuracy of 99.00 ± 3.16% and a recall of 97.50 ± 7.91%. The main innovation of this research is the combination of machine-learning approaches with non-destructive methods to analyze energy variations in ultrasonic signals and to enable early damage detection in cement mortars with different cement content ratios. This data-driven, reproducible analytical framework not only improves the accuracy and reliability of damage identification but is also extensible for health monitoring and continuous assessment of concrete structures at practical scales, serving as an effective tool to enhance the durability and safety of civil infrastructure.

کلیدواژه‌ها English

Ultrasonic Testing, Machine Learning, K-Nearest Neighbors (KNN), Naï
ve Bayes, Random Forest
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