دانلود مقاله مدل پیش بینی خواص بتن با روش های یادگیری ماشینی و یادگیری عمیق
چکیده
1. مقدمه
2. هوش مصنوعی، یادگیری ماشینی و یادگیری عمیق
3. نتایج و بحث
4. نتیجه گیری و پیشنهادات
اعلامیه منافع رقابتی
نامگذاری
در دسترس بودن داده ها
منابع
Abstract
1. Introduction
2. Artificial intelligence, machine learning, and deep learning
3. Results and discussion
4. Conclusions and recommendations
Declaration of competing interest
Nomenclature
Data availability
References
چکیده
بتن یکی از پرکاربردترین مصالح در کاربردهای مختلف مهندسی عمران است. نرخ تولید جهانی آن برای پاسخگویی به تقاضا در حال افزایش است. خواص مکانیکی بتن از جمله پارامترهای مهم در طراحی و ارزیابی عملکرد آن است. در طول چند دهه گذشته، یادگیری ماشینی برای مدل‌سازی مشکلات دنیای واقعی استفاده شده است. یادگیری ماشینی، به عنوان شاخه ای از هوش مصنوعی، در بسیاری از زمینه های علمی مانند رباتیک، آمار، بیوانفورماتیک، علوم کامپیوتر و مصالح ساختمانی محبوبیت پیدا می کند. یادگیری ماشینی مزایای زیادی نسبت به مدل های آماری و تجربی دارد، مانند دقت بهینه، سرعت عملکرد بالا، پاسخگویی در محیط های پیچیده و مقرون به صرفه بودن اقتصادی. اخیراً، محققان بیشتری به یادگیری عمیق، که گروهی از الگوریتم‌های یادگیری ماشینی است، به عنوان روشی قدرتمند در مسائل تشخیص و طبقه‌بندی نگاه می‌کنند. از این رو، این مقاله مروری بر کاربردهای موفق مدل ML و DL برای پیش‌بینی خواص مکانیکی بتن ارائه می‌کند. چندین الگوریتم مدل‌سازی مورد بررسی قرار گرفتند که کاربردها، عملکرد، شکاف‌های دانش فعلی و پیشنهادهایی برای تحقیقات آینده را برجسته می‌کردند. این مقاله به مهندسان و محققان مصالح ساختمانی در انتخاب تکنیک‌های مناسب و دقیق که متناسب با کاربردهایشان باشد، کمک می‌کند.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
Concrete is one of the most widely used materials in various civil engineering applications. Its global production rate is increasing to meet demand. Mechanical properties of concrete are among important parameters in designing and evaluating its performance. Over the past few decades, machine learning has been used to model real-world problems. Machine learning, as a branch of artificial intelligence, is gaining popularity in many scientific fields such as robotics, statistics, bioinformatics, computer science, and construction materials. Machine learning has many advantages over statistical and experimental models, such as optimal accuracy, high-performance speed, responsiveness in complex environments, and economic cost-effectiveness. Recently, more researchers are looking into deep learning, which is a group of machine learning algorithms, as a powerful method in matters of diagnosis and classification. Hence, this paper provides a review of successful ML and DL model applications to predict concrete mechanical properties. Several modeling algorithms were reviewed highlighting their applications, performance, current knowledge gaps, and suggestions for future research. This paper will assist construction material engineers and researchers in selecting suitable and accurate techniques that fit their applications.
Introduction
Concrete is the most widely used building material worldwide. With population growth and urbanization, the demand for concrete is expected to reach 18 billion by 2050 [[1], [2], [3]]. In order to improve the design of concrete structures, it is necessary to gain a better understanding of concrete performance, relying on accurate evaluation of its mechanical properties. Among the various properties of concrete, compressive strength has been considered a direct indicator of performance. It is directly related to the safety and performance of the structure throughout its life cycle [1,4,5].
Concrete is a complex system of combinations of different components (coarse and fine aggregates, water, cement, and additional mixtures) that are randomly distributed throughout the concrete matrix [[6], [7], [8], [9]]. This heterogeneous feature makes it difficult to accurately predict certain mechanical properties, especially compressive strength [[10], [11], [12]]. The most direct way to evaluate the compressive strength of concrete is through physical tests performed on specimens cured to the desired age [13,14]. Such a method for evaluating the compressive strength needs time while being affected by other factors related to specimen fabrications and test operators. Moreover, the test tends to damage the specimens. Empirical regression methods were therefore proposed to predict compressive strength [[15], [16], [17]], but the disadvantage of this method is the non-linear relationship between the concrete mixture and the concrete's compressive strength. This prevents an accurate regression expression. Numerical simulation is another method that can predict the behavior of concrete. However, a good prediction of concrete behavior is not easy to achieve due to the non-linearity and randomness [[18], [19], [20]].
Conclusions and recommendations
The present study investigated the performance of different ML/DL models to predict the mechanical properties of concrete. Among the wide range of ML/DL models, support vector machines, decision trees, evolutionary algorithms, and artificial neural networks were examined due to the popularity of these models as well as their frequent use in the field of civil engineering. Therefore, the performance of these models in various studies to evaluate the compressive strength, tensile strength, shear strength, flexural strength, and elastic modulus was evaluated. A comparison of experimental models with ML/DL methods shows that ML/DL models with better updating capability and the ability to analyze large datasets perform better. On the other hand, statistical models are not good for predicting complex structures due to their high cost and time-consuming nature. According to ML/DL methods, selecting the appropriate model for predicting the target strength of concrete should be made by considering different criteria. A review of various studies shows that the relationship between concrete components and mechanical strength is influential in choosing the forecasting model. Therefore, models that can respond in nonlinear space should be used if the relationship is nonlinear. In these cases, SVM and ANN models can be used for their acceptable performance in a non-linear environment with fewer errors. To achieve more accurate results, optimization of these models with metaheuristic algorithms can also be used. But if we need the transparency of the model and the explicit mathematical formula between input and output, we can use decision tree models and evolutionary algorithms. On the other hand, the use of ensemble models to optimize these models, although it results in higher accuracy, increases computation time and model complexity. The combined SVM and ANN models, although they increase the computation time, have accurate results in the face of extensive data. Therefore, based on the study, the best option for predicting the strength of concrete in terms of model accuracy and model implementation is the combined use of SVM and ANN combined models.