Infrastructure Engineering - Research Publications

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    Prediction of the post-failure behavior of rocks: Combining artificial intelligence and acoustic emission sensing
    Yousefpour, N ; Pouragha, M (WILEY, 2022-07)
    Abstract Acoustic emission (AE) reading is among the most common methods for monitoring the behavior of brittle materials such as rock and concrete. This study uses discrete element method (DEM) simulations to explore the correlations between the pre‐failure AE readings with the post‐failure behavior and residual strength of rock masses. The deep learning (DL) method based on long short‐term memory (LSTM) algorithms has been applied to generate predictive models based on the data from DEM simulations of biaxial compression. The dataset has been populated by varying interparticle friction while keeping bond cohesion constant. Various configurations of the LSTM algorithm were evaluated considering different scenarios for input features (strain, stress, and AE energy records) and a range of values for the key hyperparameters. The prime AI models show promising accuracy in predicting residual strength decay with strain based on pre‐failure patterns in AE readings. The results indicate that the pre‐failure AE indeed encapsulates information about the developing failure mechanisms and the post‐failure response in rocks, which can be captured through artificial intelligence.
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    Quantitative contribution of the Grain for Green Program to vegetation greening and its spatiotemporal variation across the Chinese Loess Plateau
    Song, Y ; Wang, Y ; Jin, L ; Shi, W ; Aryal, J ; Comber, A (WILEY, 2022-07-15)
    Abstract A distinct greening trend is evident in Asia, especially on the Loess Plateau (LP) of China, which is driven by climate change and large‐scale land‐use‐related ecological projects, especially the 'Grain for Green Program' (GFGP). However, the specific contributions of the GFGP to vegetation greening and the variation of this greening on a large spatiotemporal scale are not yet clear. We used long‐time‐series normalized difference vegetation index datasets and climate datasets based on the double mass curve method to quantify the contributions of ecological projects and climate change to the greening trend on the LP. We found that the interannual fluctuation of vegetation change was likely related to the interannual fluctuation of climate, especially precipitation. The increasing trend of vegetation change after 2005 indicated that the GFGP, as a type of external disturbance, began to improve vegetation growth. The GFGP failed initially to make a positive contribution in the first few years because of the drought conditions in 1999 and 2005. The increased precipitation played a critical role in enhancing the output of the GFGP on the LP after 2005. Then, the contribution of the GFGP increased quickly until 2013, after which it remained stable and reached average values of 58.8% ± 19.34% and 31.7% ± 24.3% in the representative areas that conducted the GFGP and in other regions with a lower implementation intensity of the GFGP, respectively. Our results highlight the contribution the GFGP has made to spatiotemporal variation due to the spatial heterogeneity of the projects, their intensity and the effect of forest stand age.
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    Modeling consolidation of soft clay by developing a fractional differential constitutive model in conjunction with an intelligent displacement inversion method
    Liu, Z ; Hu, W ; Ming, W ; Xiong, S ; Zhou, C ; Zhang, L ; Samui, P (PUBLIC LIBRARY SCIENCE, 2022-09-30)
    Studying the constitutive relation of soft clays is of critical importance for fundamentally understanding their complex consolidation behavior. This study proposes a fractional differential constitutive model in conjunction with an intelligent displacement inversion method based on the classic particle swarm optimization for modeling the deformation behavior of soft clay. The model considered the rheological properties of soft clay at different consolidation stages. In addition, statistical adaptive dynamic particle swarm optimization-least squares support vector machines were implemented to identify the model parameters efficiently. The accuracy and effectiveness of the model were validated using available experimental results. Finally, the application results showed that the proposed model could efficiently simulate coupling properties of soft clay's primary and secondary consolidations.
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    Modelling attenuation of irregular wave fields by artificial ice floes in the laboratory
    Toffoli, A ; Pitt, JPA ; Alberello, A ; Bennetts, LG (ROYAL SOC, 2022-10-31)
    A summary is given on the utility of laboratory experiments for gaining understanding of wave attenuation in the marginal ice zone, as a complement to field observations, theory and numerical models. It is noted that most results to date are for regular incident waves, which, combined with the highly nonlinear wave-floe interaction phenomena observed and measured during experimental tests, implies that the attenuation of regular waves cannot necessarily be used to infer the attenuation of irregular waves. Two experiments are revisited in which irregular wave tests were conducted but not previously reported, one involving a single floe and the other a large number of floes, and the transmission coefficients for the irregular and regular wave tests are compared. The transmission spectra derived from the irregular wave tests agree with the regular wave data but are overpredicted by linear models due to nonlinear dissipative processes, regardless of floe configuration. This article is part of the theme issue 'Theory, modelling and observations of marginal ice zone dynamics: multidisciplinary perspectives and outlooks'.
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    Pedestrian Origin-Destination Estimation Based on Multi-Camera Person Re-Identification
    Li, Y ; Sarvi, M ; Khoshelham, K ; Zhang, Y ; Jiang, Y (MDPI, 2022-10)
    Pedestrian origin-destination (O-D) estimates that record traffic flows between origins and destinations, are essential for the management of pedestrian facilities including pedestrian flow simulation in the planning phase and crowd control in the operation phase. However, current O-D data collection techniques such as surveys, mobile sensing using GPS, Wi-Fi, and Bluetooth, and smart card data have the disadvantage that they are either time consuming and costly, or cannot provide complete O-D information for pedestrian facilities without entrances and exits or pedestrian flow inside the facilities. Due to the full coverage of CCTV cameras and the huge potential of image processing techniques, we address the challenges of pedestrian O-D estimation and propose an image-based O-D estimation framework. By identifying the same person in disjoint camera views, the O-D trajectory of each identity can be accurately generated. Then, state-of-the-art deep neural networks (DNNs) for person re-ID at different congestion levels were compared and improved. Finally, an O-D matrix based on trajectories was generated and the resident time was calculated, which provides recommendations for pedestrian facility improvement. The factors that affect the accuracy of the framework are discussed in this paper, which we believe could provide new insights and stimulate further research into the application of the Internet of cameras to intelligent transport infrastructure management.
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    Application of Machine Learning to Ranking Predictors of Anti-VEGF Response.
    Arslan, J ; Benke, KK (MDPI AG, 2022-11-18)
    Age-related macular degeneration (AMD) is a heterogeneous disease affecting the macula of individuals and is a cause of irreversible vision loss. Patients with neovascular AMD (nAMD) are candidates for the anti-vascular endothelial growth factor (anti-VEGF) treatment, designed to regress the growth of abnormal blood vessels in the eye. Some patients fail to maintain vision despite treatment. This study aimed to develop a prediction model based on features weighted in order of importance with respect to their impact on visual acuity (VA). Evaluations included an assessment of clinical, lifestyle, and demographic factors from patients that were treated over a period of two years. The methods included mixed-effects and relative importance modelling, and models were tested against model selection criteria, diagnostic and assumption checks, and forecasting errors. The most important predictors of an anti-VEGF response were the baseline VA of the treated eye, the time (in weeks), treatment quantity, and the treated eye. The model also ranked the impact of other variables, such as intra-retinal fluid, haemorrhage, pigment epithelium detachment, treatment drug, baseline VA of the untreated eye, and various lifestyle and demographic factors. The results identified variables that could be targeted for further investigation in support of personalised treatments based on patient data.
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    Application of 24h Dynamic Electrocardiography in the Diagnosis of Asymptomatic Myocardial Ischemia with Arrhythmia in Elderly Patients with Coronary Heart Disease
    Chen, Z ; Tan, H ; Liu, X ; Tang, M ; Li, W (HINDAWI LTD, 2022-11-11)
    OBJECTIVE: To investigate the application effect of 24 h dynamic electrocardiogram in the diagnosis of asymptomatic myocardial ischemia with arrhythmia in elderly patients with coronary heart disease. METHODS: A total of 206 elderly patients suspected of coronary heart disease (CHD) with asymptomatic myocardial ischemia and arrhythmia were selected as the research subjects. 24 h dynamic electrocardiogram and conventional electrocardiogram examinations were conducted. Coronary angiography was used as the gold standard to observe the performance of the two examination methods in the diagnosis of asymptomatic myocardial ischemia with arrhythmia in elderly patients with CHD. RESULTS: Coronary angiography showed 174 positive cases and 32 negative cases among the 206 patients. The diagnostic results of a conventional electrocardiogram showed 150 positive cases and 20 negative cases. Its sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 86.21%, 62.50%, 82.52%, 92.59%, and 45.45%, respectively. The diagnostic results of 24 h dynamic electrocardiograms showed 168 positive cases and 29 negative cases. Its sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 96.55%, 96.63%, 95.63%, 98.25%, and 82.86%, respectively. The above results indicated that 24 h dynamic electrocardiogram was significantly better (P < 0.05). The detection rate of arrhythmia types by 24-hour dynamic electrocardiogram was significantly higher than that of conventional electrocardiogram (P < 0.05). CONCLUSION: 24 h dynamic electrocardiogram is helpful for the diagnosis of asymptomatic myocardial ischemia with arrhythmia in elderly patients with CHD and can improve the detection rate, thereby providing a basis for clinical diagnosis and treatment.
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    A ratio fluorescence method based on dual emissive gold nanoclusters for detection of biomolecules and metal ions
    Kong, C ; Luo, Y ; Zhang, W ; Lin, T ; Na, Z ; Liu, X ; Xie, Z (ROYAL SOC CHEMISTRY, 2022-04-13)
    Gold nanoclusters have good biocompatibility and can be easily modified to improve their luminescence properties. In this study, we prepared a new type of dual-emitting gold nanoclusters (d-Au NCs) for discriminative detection of phenylalanine and Fe3+ with high selectivity and sensitivity. The fluorescence sensor which was synthesized without any further assembly or conjugation shows dual-emissions at 430 nm and 600 nm under a single excitation at 350 nm. Phenylalanine can turn on the red emission of the probe, while Fe3+ can turn on its yellow emission and turn off the red emission. By detecting a variety of amino acids and metal ions, d-Au NCs showed good selectivity to phenylalanine and Fe3+. Finally, this method was applied to determine phenylalanine and Fe3+ in lake water, human urine and milk, which has certain application prospects in the field of biology and environment.
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    Fruit classification using attention-based MobileNetV2 for industrial applications
    Shahi, TB ; Sitaula, C ; Neupane, A ; Guo, W ; Bianconi, F (PUBLIC LIBRARY SCIENCE, 2022-02-25)
    Recent deep learning methods for fruits classification resulted in promising performance. However, these methods are with heavy-weight architectures in nature, and hence require a higher storage and expensive training operations due to feeding a large number of training parameters. There is a necessity to explore lightweight deep learning models without compromising the classification accuracy. In this paper, we propose a lightweight deep learning model using the pre-trained MobileNetV2 model and attention module. First, the convolution features are extracted to capture the high-level object-based information. Second, an attention module is used to capture the interesting semantic information. The convolution and attention modules are then combined together to fuse both the high-level object-based information and the interesting semantic information, which is followed by the fully connected layers and the softmax layer. Evaluation of our proposed method, which leverages transfer learning approach, on three public fruit-related benchmark datasets shows that our proposed method outperforms the four latest deep learning methods with a smaller number of trainable parameters and a superior classification accuracy. Our model has a great potential to be adopted by industries closely related to the fruit growing and retailing or processing chain for automatic fruit identification and classifications in the future.
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    A Hybrid Feature Extraction Method for Nepali COVID-19-Related Tweets Classification
    Shahi, TB ; Sitaula, C ; Paudel, N ; G, TR (HINDAWI LTD, 2022-03-09)
    COVID-19 is one of the deadliest viruses, which has killed millions of people around the world to this date. The reason for peoples' death is not only linked to its infection but also to peoples' mental states and sentiments triggered by the fear of the virus. People's sentiments, which are predominantly available in the form of posts/tweets on social media, can be interpreted using two kinds of information: syntactical and semantic. Herein, we propose to analyze peoples' sentiment using both kinds of information (syntactical and semantic) on the COVID-19-related twitter dataset available in the Nepali language. For this, we, first, use two widely used text representation methods: TF-IDF and FastText and then combine them to achieve the hybrid features to capture the highly discriminating features. Second, we implement nine widely used machine learning classifiers (Logistic Regression, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, Decision Trees, Random Forest, Extreme Tree classifier, AdaBoost, and Multilayer Perceptron), based on the three feature representation methods: TF-IDF, FastText, and Hybrid. To evaluate our methods, we use a publicly available Nepali-COVID-19 tweets dataset, NepCov19Tweets, which consists of Nepali tweets categorized into three classes (Positive, Negative, and Neutral). The evaluation results on the NepCOV19Tweets show that the hybrid feature extraction method not only outperforms the other two individual feature extraction methods while using nine different machine learning algorithms but also provides excellent performance when compared with the state-of-the-art methods.