Damage Detection in Structural Health Monitoring using Hybrid Convolution Neural Network and Recurrent Neural Network

The process of damage identification in Structural Health Monitoring (SHM) gives us a lot of practical information about the current status of the inspected structure. The target of the process is to detect damage status by processing data collected from sensors, followed by identifying the difference between the damaged and the undamaged states. Different machine learning techniques have been applied to attempt to extract features or knowledge from vibration data, however, they need to learn prior knowledge about the factors affecting the structure. In this paper, a novel method of structural damage detection is proposed using convolution neural network and recurrent neural network. A convolution neural network is used to extract deep features while recurrent neural network is trained to learn the long-term historical dependency in time series data. This method with combining two types of features increases discrimination ability when compares with it to deep features only. Finally, the neural network is applied to categorize the time series into two states - undamaged and damaged. The accuracy of the proposed method was tested on a benchmark dataset of Z24-bridge (Switzerland). The result shows that the hybrid method provides a high level of accuracy in damage identification of the tested structure

Multi-level damage detection using a combination of deep neural networks

In recent years, bridge damage identification using a convolutional neural network (CNN) has become a hot research topic and received much attention in the field of civil engineering. Although CNN is capable of categorizing damaged and undamaged states from the measured data, the level of accuracy for damage diagnosis is still insufficient due to the tendency of CNN to ignore the temporal dependency between data points. To address this problem, this paper introduces a novel hybrid damage detection method based on the combination of CNN and Long Short-Term Memory (LSTM) to classify and quantify different levels of damage in the bridge structure. In this method, the CNN model will be used to extract the spatial damage features, which will be combined with the temporal features obtained from Long Short-Term Memory (LSTM) model to create the enhanced damage features. The combination successfully strengthened the damage detection capability of the neural network. Moreover, deep learning is also improved in this paper to process the acceleration-time data, which has a different amplitude at short intervals and the same amplitude at long intervals. The empirical result on the Vang bridge shows that our hybrid CNN-LSTM can detect structural damage with a high level of accuracy

Correlation between Resilient Modulus MR and Deviator Stress for Subgrade soils of northern provinces in Vietnam

Recently, Resilient Modulus is applied in pavement structural design in Vietnam. The resilient modulus of subgrade soil is an essential input parameter for a flexible pavement design. The resilient modulus depends on soil properties, stress state, and soil type.  However, there is limited research on resilient modulus of soils and models to estimate resilient modulus in Vietnam. Therefore, in this study, soil samples were collected from two provinces in northern Vietnam, namely Bac Giang province and Ninh Binh province, and then physical and mechanical tests were conducted for these samples. In addition, a series of cyclic triaxial tests also conducted according to AASHTO T307 specification to obtain resilient modulus of these soils.  The results showed that the resilient modulus decreased with the increase of deviator stress for Bac Giang samples and increased with the increase of deviator stress in the case of Ninh Binh samples. Simple deviator stress models have developed to estimate a resilient modulus of soils in the area

Correlation between Resilient Modulus MR and Deviator Stress for Subgrade soils of northern provinces in Vietnam

Recently, Resilient Modulus is applied in pavement structural design in Vietnam. The resilient modulus of subgrade soil is an essential input parameter for a flexible pavement design. The resilient modulus depends on soil properties, stress state, and soil type.  However, there is limited research on resilient modulus of soils and models to estimate resilient modulus in Vietnam. Therefore, in this study, soil samples were collected from two provinces in northern Vietnam, namely Bac Giang province and Ninh Binh province, and then physical and mechanical tests were conducted for these samples. In addition, a series of cyclic triaxial tests also conducted according to AASHTO T307 specification to obtain resilient modulus of these soils.  The results showed that the resilient modulus decreased with the increase of deviator stress for Bac Giang samples and increased with the increase of deviator stress in the case of Ninh Binh samples. Simple deviator stress models have developed to estimate a resilient modulus of soils in the area

Model Updating for Large-Scale Railway Bridge Using Grey Wolf Algorithm and Genetic Alghorithms

This paper proposes a novel hybrid algorithm to deal with an inverse problem of a large-scale truss bridge. Grey Wolf Optimization (GWO) Algorithm is a well-known and widely applied metaheuristic algorithm. Nevertheless, GWO has two major drawbacks. First, this algorithm depends crucially on the positions of the leading Wolf. If the position of the leaderis far from the best solution, the obtained results are poor. On the other hand, GWO does not own capacities to improve the quality of new generations if elements are trapped into local minima. To remedy the shortcomings of GWO, we propose a hybrid algorithm combining GWO with Genetic Algorithm (GA), termed HGWO-GA. This proposed method contains two key features (1) based on crossover and mutation capacities, GA is first utilized to generate the high-quality elements (2) after that, the optimization capacity of GWO is employed to seek the optimal solutions. To assess the effectiveness of the proposed approach, a large-scale truss bridge is employed for model updating. The obtained results show that HGWO-GA not only provides a good agreement between numerical and experimental results but also outperforms traditional GWO in terms of accuracy

TÀI NGUYÊN VỊ THẾ CỤM ĐẢO THỔ CHU, PHÍA NAM VIỆT NAM

Located near center of the Gulf of Thailand, the Tho Chu island group is approximately far 160 km from Ca Mau southwestwards, and 100 km from Phu Quoc island northwestwards. This group consists of 8 islands, of which the largest Tho Chu island is of 12.15 km2 wide and 167 m high, and composes of clastic sedimentary rocks. Despite the greatest distance from the coast, this coastal island group has the characteristics of geological structure, morphology, spatial structure, area, height, ecological landscape, dynamics and stability of natural processes that create the great value of geo-natural position resources and favorable residential environment. Regarding value of geo-economic position resources, the island group belongs to Phu Quoc district, but it meets the good conditions to become a district-level administrative unit belonging to Kien Giang province. This is a priority site for sea-island economic development, the island group has a great potential to develop the marine economic sectors such as fisheries, natural conservation, tourism and other important services such as oil and gas extraction, navigation and search-rescue at sea. In terms of value of geo-political position resources, this island group includes Hon Nhan islet as the basic point of A1, so has the great value for Vietnam’s sovereignty, sovereign rights and interests in the Gulf of Thailand. Situating in the politically high sensitive region, the island group possesses the high value on the defense, could develop into a firmly military outpost contributing to national defense and security at sea.Nằm ở gần trung tâm vịnh Thái Lan, cụm đảo Thổ Chu cách mũi Cà Mau khoảng 160 km về phía tây nam, cách đảo Phú Quốc khoảng 100 km về phía tây bắc. Cụm đảo gồm 8 hòn, lớn nhất là đảo Thổ Chu rộng 12,15 km2, cao 167 m, cấu tạo từ đá trầm tích vụn thô. Mặc dù là cụm đảo ven bờ xa bờ nhất của Việt Nam ở vịnh Thái Lan, nhưng các đặc điểm về cấu tạo địa chất, hình thể và cấu trúc không gian, diện tích, độ cao và cảnh quan sinh thái; động lực và tính ổn định của các quá trình tự nhiên,… đã tạo ra giá trị lớn về tài nguyên địa-tự nhiên và môi trường sinh cư thuận lợi cho cụm đảo. Về giá trị tài nguyên vị thế địa-kinh tế, cụm đảo thuộc huyện Phú Quốc, nhưng hội tụ đủ các điều kiện trở thành đơn vị hành chính cấp huyện, thuộc tỉnh Kiên Giang. Đây là vị trí ưu tiên đối với phát triển kinh tế biển-đảo của đất nước, là địa bàn tiềm năng lớn phát triển các lĩnh vực kinh tế biển như thủy sản, bảo tồn thiên nhiên, du lịch và các dịch vụ quan trọng khác như khai thác dầu khí, hàng hải và tìm kiến-cứu nạn trên biển. Về giá trị tài nguyên vị thế địa-chính trị, cụm đảo có Hòn Nhạn là điểm cơ sở A1 có giá trị vô cùng to lớn về chủ quyền, quyền chủ quyền và lợi ích quốc gia trên vịnh Thái Lan. Nằm trong vùng địa-chính trị nhạy cảm cao, cụm đảo có giá trị lớn về phòng thủ, có thể phát triển thành một cụm cứ điểm quân sự vững chắc, góp phần bảo vệ Tổ quốc và đảm bảo an ninh trên biển

ARSENIC POLLUTION IN TUBE WELL WATER AT HANOI SUBURB VILLAGES

Joint Research on Environmental Science and Technology for the Eart

Applications of artificial intelligence to prostate multiparametric MRI (mpMRI): Current and emerging trends

Prostate carcinoma is one of the most prevalent cancers worldwide. Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification. Machine learning (ML), a branch of artificial intelligence, can rapidly and accurately analyze mpMRI images. ML could provide better standardization and consistency in identifying prostate lesions and enhance prostate carcinoma management. This review summarizes ML applications to prostate mpMRI and focuses on prostate organ segmentation, lesion detection and segmentation, and lesion characterization. A literature search was conducted to find studies that have applied ML methods to prostate mpMRI. To date, prostate organ segmentation and volume approximation have been well executed using various ML techniques. Prostate lesion detection and segmentation are much more challenging tasks for ML and were attempted in several studies. They largely remain unsolved problems due to data scarcity and the limitations of current ML algorithms. By contrast, prostate lesion characterization has been successfully completed in several studies because of better data availability. Overall, ML is well situated to become a tool that enhances radiologists\u27 accuracy and speed

Current status of fish-borne zoonotic trematode infections in Gia Vien district, Ninh Binh province, Vietnam

BACKGROUND: Ninh Binh province is known as an endemic area of fish-borne zoonotic trematode (FZT) transmission in Vietnam. A cross-sectional study was conducted in Gia Minh and Gia Thinh communes of Gia Vien district, Ninh Binh province to investigate the infections with different stages of FZT in various host species. METHODS: Faecal samples from 1,857 humans were examined for trematode eggs using the Kato-Katz method, while faecal samples from 104 dogs, 100 cats, and 100 pigs were examined using the Formalin-ethyl acetate concentration technique (FECT). A total of 483 specimens of freshwater fish, representing 9 species, were examined for metacercariae using the artificial digestion method. Three methods of cercarial detection (shedding, crushing and cutting) were applied for examination of 3,972 specimens of freshwater snails, representing 7 species. All relevant data e.g. location, sex, age group, animal species, and habitat were recorded for statistical analyses. RESULTS: Helminth eggs were found in 65.5% of the human faecal samples, including 20.5% of faecal samples containing small trematode eggs. Infection with small trematodes differed among communes, age groups and sexes. Eggs of small trematodes were found in 32.7% of faecal samples from dogs, 49.0% from cats and 13.0% from pigs. The difference in prevalences and intensities were significant among species of animals but did not differ between the two communes. All fish species were infected with FZT, with an average prevalence of 56.1% and a mean intensity of 33.245 metacercariae per gram. Prevalence and intensity in fish differed significantly among cummunes and fish groups. Six species of zoonotic trematodes were identified. Metacercariae of the small liver fluke, Clonorchis sinensis, was only found in Hemiculter leucisculus. A total of 9 specimens from two snail species, Melanoides tuberculata and Bithynia fuchsiana, were infected with trematodes and four cercarial types were detected in the study sites. CONCLUSIONS: We conclude that Gia Minh and Gia Thinh communes are continuing to be hot-spot endemic areas of FZT and other helminths infections where the habit of eating raw fish by the local people is still present