The effect of metal corrosion on the structural reliability of the Pre-Engineered steel frame

Nowadays, Pre-Engineered steel buildings are widely used in the field of the industrial construction. However, design standards often only care about the safety (or reliability) at the start time but not concerned about the deterioration of reliability during used under the metal corrosive of environment. Meanwhile, reliability and durability of steel structure depend heavily on metal corrosion of environmental, this is uncertainty parameters. In this research presents the effect of the safety of Pre-Engineered steel frames considering metal corrosion. The metal corrosion modeling used to propose by M.E. Komp. Reliability of the structure is evaluated using Monte Carlo simulation method and Finite Element Method (FEM). This computer program is written by using the MATLAB programming language. The results numbers are reliability and durability behaviors under corrosion are determined for exposure about from 10 - 50 years. Effects of input parameters are also investigated

Petrographic Characteristics and Depositional Environment Evolution of Middle Miocene Sediments in the Thien Ung - Mang Cau Structure of Nam Con Son Basin

This paper introduces the petrographic characteristics and depositional environment of Middle Miocene rocks of the Thien Ung - Mang Cau structure in the central area of Nam Con Son Basin based on the results of analyzing thin sections and structural characteristics of core samples. Middle Miocene sedimentary rocks in the studied area can be divided into three groups: (1) Group of terrigenous rocks comprising greywacke sandstone, arkosic sandstone, lithic-quartz sandstone, greywacke-lithic sandstone, oligomictic siltstone, and bitumenous claystone; (2) Group of carbonate rocks comprising dolomitic limestone and bituminous limestone; (3) Mixed group comprising calcareous sandstone, calcarinate sandstone, arenaceous limestone, calcareous claystone, calcareous silty claystone, dolomitic limestone containing silt, and bitumen. The depositional environment is expressed through petrographic characteristics and structure of the sedimentary rocks in core samples. The greywacke and arkosic sandstones are of medium grain size, poor sorting and roundness, and siliceous cement characterizing the alluvial and estuarine fan environment expressed by massive structure of core samples. The mixed calcareous limestone, arenaceous dolomitic limestone, and calcareous and bituminous clayey siltstone in the core samples are of turbulent flow structure characterizing shallow bay environment with the action of bottom currents. The dolomitic limestones are of relatively homogeneous, of microgranular and fine-granular texture, precipitated in a weakly reducing, semi-closed, and relatively calm bay environment

Anammox treatment performances using polyethylene sponge as a biomass carrier

Nitrogen removal using a polyethylene (PE) sponge biomass carrier was evaluated in a fixed-bed reactor for nitrogen removal by the anammox process. The fixed-bed reactor was operated continuously for 240 days. Average T-N removal efficiencies of each period increased from 38 % to 67 %, 72 %, 74 % to 75 % with stepwise increases in volumetric T-N loading rates. A T-N removal rate of 2.8 kg N/m3/day was obtained after 240 days of operation. After 3 months, anammox biomass fully covered the surface of the PE sponge carrier and the color of the material changed from white to red. Following 5 months of operation, biomass proliferated on the surface of the material and a dark-red color was observed. These results shown that anammox process using PE sponge materials as biomass carriers in the fixed-bed reactor will be suitable for NH4-N removal from wastewater containing high NH4-N. However, it is necessary to investigate whether PE sponge material can operate under high organic carbon concentrations in anammox process, because these wastewaters always contain high concentration of organic matter

Data-driven structural health monitoring using feature fusion and hybrid deep learning

Smart structural health monitoring (SHM) for large-scale infrastructures is an intriguing subject for engineering communities thanks to its significant advantages such as timely damage detection, optimal maintenance strategy, and reduced resource requirement. Yet, it is a challenging topic as it requires handling a large amount of collected sensors data continuously, which is inevitably contaminated by random noises. Therefore, this study developed a practical end-to-end framework that makes use of physical features embedded in raw data and an elaborated hybrid deep learning model, namely 1DCNN-LSTM, featuring two algorithms - Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM). In order to extract relevant features from sensory data, the method combines various signal processing techniques such as the autoregressive model, discrete wavelet transform, and empirical mode decomposition. The hybrid deep learning 1DCNN-LSTM is designed based on the CNN’s capacity of capturing local information and the LSTM network’s prominent ability to learn long-term dependencies. Through three case studies involving both experimental and synthetic datasets, it is demonstrated that the proposed approach achieves highly accurate damage detection, as accurate as the powerful two-dimensional CNN, but with a lower time and memory complexity, making it suitable for real-time SHM

Cumulative Quality Modeling for HTTP Adaptive Streaming

Thanks to the abundance of Web platforms and broadband connections, HTTP Adaptive Streaming has become the de facto choice for multimedia delivery nowadays. However, the visual quality of adaptive video streaming may fluctuate strongly during a session due to bandwidth fluctuations. So, it is important to evaluate the quality of a streaming session over time. In this paper, we propose a model to estimate the cumulative quality for HTTP Adaptive Streaming. In the model, a sliding window of video segments is employed as the basic building block. Through statistical analysis using a subjective dataset, we identify three important components of the cumulative quality model, namely the minimum window quality, the last window quality, and the average window quality. Experiment results show that the proposed model achieves high prediction performance and outperforms related quality models. In addition, another advantage of the proposed model is its simplicity and effectiveness for deployment in real-time estimation. The source code of the proposed model has been made available to the public at https://github.com/TranHuyen1191/CQM

Learning evolving relations for multivariate time series forecasting

Multivariate time series forecasting is essential in various fields, including healthcare and traffic management, but it is a challenging task due to the strong dynamics in both intra-channel relations (temporal patterns within individual variables) and inter-channel relations (the relationships between variables), which can evolve over time with abrupt changes. This paper proposes ERAN (Evolving Relational Attention Network), a framework for multivariate time series forecasting, that is capable to capture such dynamics of these relations. On the one hand, ERAN represents inter-channel relations with a graph which evolves over time, modeled using a recurrent neural network. On the other hand, ERAN represents the intra-channel relations using a temporal attentional convolution, which captures the local temporal dependencies adaptively with the input data. The elvoving graph structure and the temporal attentional convolution are intergrated in a unified model to capture both types of relations. The model is experimented on a large number of real-life datasets including traffic flows, energy consumption, and COVID-19 transmission data. The experimental results show a significant improvement over the state-of-the-art methods in multivariate time series forecasting particularly for non-stationary data

Required flows for aquatic ecosystems in Ma River, Vietnam

Ecological flow requirements for the Ma River in dry season were assessed in three reaches of Ma – Buoi, Ma – Len and Ma – Chu. 5 indictor fish species was chosen based on biodiversity survey and roles of those species in aquatic ecosystem as well as local communities. Biological and hydrological data (dry season of 2016- 2017) and 35 year recorded hydrological data were collected and analyzed as input data for a physical habitat model River HYdraulic and HABitat SImulation Model – RHYHABSIM. Model results shown that the optimal flows of the reaches were very much higher compare with the minimum annual low flow - MALF. In this study, MALF7day were applied to calculate the recommended minimum flows of the three reaches. The recommended required minimum flows for Ma – Buoi, Ma – Len and Ma – Chu reaches were 51 m3/s, 49 m3/s and 61 m3/s, respectively. It must be stressed that this study only assessed whether or not there is enough habitat available for the river to sustain a healthy ecosystem