Structural Health Monitoring of RC structures using optic fiber strain measurements: a deep learning approach

This paper reports the early findings of an ongoing project aimed at developing new methods to upgrade the current maintenance strategies of the civil and transport infrastructure. As part of these new methods, the use of Machine Learning (ML) algorithms is being investigated to constitute the core of a new generation of more accurate and robust structural health monitoring (SHM) systems for concrete structures. Unlike most of the existing SHM systems, relying on the analysis of the natural frequencies of the structure based on data obtained from accelerometers, the present study uses a distributed optic fiber system to monitor the strain distribution along steel reinforcing bars. The preliminary results of the study indicate that a semi-supervised Deep Autoencoder algorithm (DAE) can successfully quantify the damage attributable to transverse cracks in a reinforced concrete beam subjected to three-point loading. Future applications will feature the determination of crack locations, early detection of reinforcement corrosion as well as other types of damage such as splitting cracks or surface spalling

Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study

Some real-world domains, such as Agriculture and Healthcare, comprise early-stage disease indications whose recording constitutes a rare event, and yet, whose precise detection at that stage is critical. In this type of highly imbalanced classification problems, which encompass complex features, deep learning (DL) is much needed because of its strong detection capabilities. At the same time, DL is observed in practice to favor majority over minority classes and consequently suffer from inaccurate detection of the targeted early-stage indications. To simulate such scenarios, we artificially generate skewness (99% vs. 1%) for certain plant types out of the PlantVillage dataset as a basis for classification of scarce visual cues through transfer learning. By randomly and unevenly picking healthy and unhealthy samples from certain plant types to form a training set, we consider a base experiment as fine-tuning ResNet34 and VGG19 architectures and then testing the model performance on a balanced dataset of healthy and unhealthy images. We empirically observe that the initial F1 test score jumps from 0.29 to 0.95 for the minority class upon adding a final Batch Normalization (BN) layer just before the output layer in VGG19. We demonstrate that utilizing an additional BN layer before the output layer in modern CNN architectures has a considerable impact in terms of minimizing the training time and testing error for minority classes in highly imbalanced data sets. Moreover, when the final BN is employed, minimizing the loss function may not be the best way to assure a high F1 test score for minority classes in such problems. That is, the network might perform better even if it is not confident enough while making a prediction; leading to another discussion about why softmax output is not a good uncertainty measure for DL models.Comment: Accepted for presentation and inclusion in ICPR 2020, the 25th International Conference on Pattern Recognitio

Structural Health Monitoring of Concrete Elements Using Deep Machine Learning

The unique nature of Structural Engineering allows the field to integrate fresh innovations in its applications only at a slow pace. However, recent advancements in networking and artificial intelligence can greatly upgrade the current processes. This thesis reports the early findings of an ongoing project aimed at developing new methods to upgrade the current maintenance strategies of the civil and transport infrastructure. As part of these new methods, the use of Machine Learning (ML) algorithms is being investigated to constitute the core of a new generation of more accurate and robust structural health monitoring (SHM) systems for concrete structures. Unlike most of the existing SHM systems, relying on the analysis of the natural frequencies of the structure based on data obtained from accelerometers, the present study uses a distributed optic fiber system to monitor the strain distribution along steel reinforcing bars. The preliminary results of the study indicate that a semi-supervised Deep Autoencoder algorithm (DAE) can successfully quantify the damage attributable to transverse cracks in a reinforced concrete beam subjected to three-point loading. Future applications will feature the determination of crack locations, early detection of reinforcement corrosion as well as other types of damage such as splitting cracks or surface spalling

Detection of objects in the images: from likelihood relationships towards scalable and efficient neural networks

Актуальность задач обнаружения и распознавания объектов на изображениях и их последовательностях с годами только возрастает. За последние несколько десятилетий предложено огромное количество подходов и методов обнаружения как аномалий, то есть областей изображения, характеристики которых отличаются от прогнозных, так и объектов интереса, о свойствах которых есть априорная информация, вплоть до библиотеки эталонов. В работе предпринята попытка системного анализа тенденций развития подходов и методов обнаружения, причин этого развития, а также метрик, предназначенных для оценки качества и достоверности обнаружения объектов. Рассмотрено обнаружение на основе математических моделей изображений. При этом особое внимание уделено подходам на основе моделей случайных полей и отношения правдоподобия. Проанализировано развитие сверточных нейронный сетей, направленных на задачи распознавания и обнаружения, включая ряд предобученных архитектур, обеспечивающих высокую эффективность при решении данной задачи. В них для обучения используются уже не математические модели, а библиотеки реальных снимков. Среди характеристик оценки качества обнаружения рассмотрены вероятности ошибок первого и второго рода, точность и полнота обнаружения, пересечение по объединению, интерполированная средняя точность. Также представлены типовые тесты, которые применяются для сравнения различных нейросетевых алгоритмовИсследование выполнено при финансовой поддержке РФФИ в рамках научного проекта № 20-17-50020 и частично проекта №19-29-09048