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deep learning for Structural Health Monitoring: Data, algorithms, Applications, Challenges, and Trends

SENSORS 2023年第21期23卷 8824-8824页

作者： Jia, Jing Li, Ying Ocean Univ China Coll Engn Dept Civil Engn Qingdao 266100 Peoples R China

Environmental effects may lead to cracking, stiffness loss, brace damage, and other damages in bridges, frame structures, buildings, etc. Structural Health Monitoring (SHM) technology could prevent catastrophic events by detecting damage early. In recent years, deep learning (DL) has developed rapidly and has been applied to SHM to detect, localize, and evaluate diverse damages through efficient feature extraction. This paper analyzes 337 articles through a systematic literature review to investigate the application of DL for SHM in the operation and maintenance phase of facilities from three perspectives: data, DL algorithms, and applications. Firstly, the data types in SHM and the corresponding collection methods are summarized and analyzed. The most common data types are vibration signals and images, accounting for 80% of the literature studied. Secondly, the popular DL algorithm types and application areas are reviewed, of which CNN accounts for 60%. Then, this article carefully analyzes the specific functions of DL application for SHM based on the facility's characteristics. The most scrutinized study focused on cracks, accounting for 30 percent of research papers. Finally, challenges and trends in applying DL for SHM are discussed. Among the trends, the Structural Health Monitoring Digital Twin (SHMDT) model framework is suggested in response to the trend of strong coupling between SHM technology and Digital Twin (DT), which can advance the digitalization, visualization, and intelligent management of SHM.

关键词： structural health monitoring deep learning algorithms damage detection data acquisition facilities

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Enhanced diagnostic accuracy for multiple lung diseases using a fine-tuned MobileNetV2 model with advanced pre-processing techniques

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 259卷

作者： Thakur, deepak Mishra, Anand Muni Singh, Jaiteg Bhardwaj, Vivek Gera, Tanya Chitkara Univ Inst Engn & Technol Rajpura Punjab India Chandigarh Grp Coll Chandigarh Coll Engn Dept Comp Sci & Engn Jhanjeri 140307 Punjab India Manipal Univ Jaipur Dept Comp Sci & Engn Jaipur Rajasthan India

Early diagnosis of lung conditions is crucial for effective treatment and improving patient health. However, traditional diagnostic methods using chest X-ray images have some notable drawbacks, such as overlapping anatomical structures obscuring areas of interest, the presence of noise potentially masking abnormalities, and low contrast diminishing differentiation. In this research undertaking, we explored an enhanced MobileNetV2 approach to augmenting the accuracy of diagnosing multiple concomitant lung pathologies. We employed an inclusive methodology, incorporating several progressive steps. We leveraged contrast-limited adaptive histogram equalization to heighten the clarity of the dataset's images. Bilateral filtering was applied to refine contrast and sharpness, along with utilizing a dense convolutional neural network. Additional techniques were utilized as well, such as image standardization, Gaussian blur, and histogram equalization, to further increase contrast and reduce noise. This was performed through rotations, scaling, horizontal flipping, brightness adjustment, elastic transformations, and random cropping and padding to generate more realistic exemplars. We defined and compiled a purpose-specific MobileNetV2 architecture for this research endeavor. The model was evaluated based on several metrics, including accuracy, precision, recall, F1-score, and specificity, after every epoch, to refine the training process. We used Bayesian optimization for more efficient fine-tuning of the model. Conditional random fields and ensemble averaging methods were employed for postprocessing. These enhancements led to superior results. After image enhancement, the model achieved an accuracy of 99.20 %, with a precision of 98.90 %, a recall of 99.10 %, and an F1-score of 99.00 %. The specificity of the predictions reached 99.40 %. These improvements assist medical workers in making more informed decisions. However, it is important to note that the current algorithm do

关键词： Chest X-ray analysis Fine-tuned MobileNetV2 deep learning algorithms Lung disease diagnosis Image preprocessing techniques Computational efficiency

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Evaluation of Solar Radiation Prediction Models Using AI: A Performance Comparison in the High-Potential Region of Konya, Türkiye

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ATMOSPHERE 2025年第4期16卷 398-398页

作者： Demir, Vahdettin KTO Karatay Univ Fac Engn & Nat Sci Civil Engn Dept TR-42020 Konya Turkiye

Solar radiation is one of the most abundant energy sources in the world and is a crucial parameter that must be researched and developed for the sustainable projects of future generations. This study evaluates the performance of different machine learning methods for solar radiation prediction in Konya, Turkey, a region with high solar energy potential. The analysis is based on hydro-meteorological data collected from NASA/POWER, covering the period from 1 January 1984 to 31 December 2022. The study compares the performance of Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), Bidirectional GRU (Bi-GRU), LSBoost, XGBoost, Bagging, Random Forest (RF), General Regression Neural Network (GRNN), Support Vector Machines (SVM), and Artificial Neural Networks (MLANN, RBANN). The hydro-meteorological variables used include temperature, relative humidity, precipitation, and wind speed, while the target variable is solar radiation. The dataset was divided into 75% for training and 25% for testing. Performance evaluations were conducted using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R2). The results indicate that LSTM and Bi-LSTM models performed best in the test phase, demonstrating the superiority of deep learning-based approaches for solar radiation prediction.

关键词： solar radiation prediction renewable energy machine learning models deep learning algorithms tree-based models Konya

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Prediction of bearing capacity of pile foundation using deep learning approaches

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Frontiers of Structural and Civil Engineering 2024年第6期18卷 870-886页

作者： Manish KUMAR Divesh Ranjan KUMAR Jitendra KHATTI Pijush SAMUI Kamaldeep Singh GROVER Department of Civil Engineering SRM Institute of Science and Technology Tiruchirappalli CampusTrichy 621105India Department of Civil Engineering National Institute of Technology PatnaBihar 800005India Department of Civil Engineering Faculty of EngineeringThammasat School of EngineeringThammasat UniversityBangkok10200Thailand Department of Civil Engineering Rajasthan Technical UniversityKota 324010India

The accurate prediction of bearing capacity is crucial in ensuring the structural integrity and safety of pile *** research compares the deep Neural Networks(DNN),Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN),Long Short-Term Memory(LSTM),and Bidirectional LSTM(BiLSTM)algorithms utilizing a data set of 257 dynamic pile load tests for the first ***,this research illustrates the multicollinearity effect on DNN,CNN,RNN,LSTM,and BiLSTM models’performance and accuracy for the first time.A comprehensive comparative analysis is conducted,employing various statistical performance parameters,rank analysis,and error matrix to evaluate the performance of these *** performance is further validated using external validation,and visual interpretation is provided using the regression error characteristics(REC)curve and Taylor *** from the comparative analysis reveal that the DNN(Coefficient of determination(R^(2))_(training(TR))=0.97,root mean squared error(RMSE)_(TR)=0.0413;R^(2)_(testing(TS))=0.9,RMSE_(TS)=0.08)followed by BiLSTM(R^(2)_(TR)=0.91,RMSE_(TR)=0.782;R^(2)_(TS)=0.89,RMSE_(TS)=0.0862)model demonstrates the highest performance *** is noted that the BiLSTM model is better than LSTM because the BiLSTM model,which increases the amount of information for the network,is a sequence processing model made up of two LSTMs,one of which takes the input in a forward manner,and the other in a backward *** prediction of pile-bearing capacity is strongly influenced by ram weight(having a considerable multicollinearity level),and the effect of the considerable multicollinearity level has been determined for the model based on the recurrent neural network *** this study,the recurrent neural network model has the least performance and accuracy in predicting the pile-bearing capacity.

关键词： deep learning algorithms high-strain dynamic pile test bearing capacity of the pile

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Cognitive load assessment through EEG: A dataset from arithmetic and Stroop tasks

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DATA IN BRIEF 2025年 60卷 111477页

作者： Nirabi, Ali Abd Rahman, Faridah Habaebi, Mohamed Hadi Sidek, Khairul Azami Yusoff, Siti Univ Islam Antarabangsa Dept Elect & Comp Engn Jalan Gombak Selangor Malaysia

This study introduces a thoughtfully curated dataset comprising electroencephalogram (EEG) recordings designed to unravel mental stress patterns through the perspective of cognitive load. The dataset incorporates EEG signals obtained from 15 subjects, with a gender distribution of 8 females and 7 males, and a mean age of 21.5 years [1]. Recordings were collected during the subjects' engagement in diverse tasks, including the Stroop color-word test and arithmetic problem-solving tasks. The recordings are categorized into four classes representing varying levels of induced mental stress: normal, low, mid, and high. Each task was performed for a duration of 10-20 s, and three trials were conducted for comprehensive data collection. Employing an OpenBCI device with an 8-channel Cyton board, the EEG captures intricate responses of the frontal lobe to cognitive challenges posed by the Stroop and Arithmetic Tests, recorded at a sampling rate of 250 Hz. The proposed dataset serves as a valuable resource for advancing research in the realm of brain-computer interfaces and offers insights into identifying EEG patterns associated with stress. The proposed dataset serves as a valuable resource for researchers, offering insights into identifying EEG patterns that correlate with different stress states. By providing a solid foundation for the development of algorithms capable of detecting and classifying stress levels, the dataset supports innovations in non-invasive monitoring tools and contributes to personalized healthcare solutions that can adapt to the cognitive states of users. This study's foundation is crucial for advancing stress classification research, with significant implications for cognitive function and well-being. (c) 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://***/licenses/by/4.0/)

关键词： EEG signals Mental stress Stress detection dataset Artificial intelligence deep learning algorithms

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deep learning-Based Bangla Text Normalization with Emotion Classification for Expressive Text-to-speech Synthesizer

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Applied Soft Computing 2025年

作者： Md. Rezaul Islam Mohammad Shahidur Rahman Department of Computer Science and Engineering Shahjalal University of Science and Technology Sylhet 3114 Bangladesh Department of Educational Technology and Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Gazipur 1750 Bangladesh

This paper presents a novel method for text normalization and emotion classification specifically designed for the preprocessing steps of Expressive Text-to-Speech (ETTS) synthesizers. Text normalization, which converts non-standard words into standardized forms, is essential for generating clear and coherent output in ETTS synthesizers. Previous studies have encountered difficulties with Bangla homographic words, prompting the development of our proposed method. Our approach begins with the creation of a tokenized and categorized dataset consisting of 26 unique semiotic classes, utilizing a rule-based method with regular expressions. This dataset is derived from the Bangla Text Normalization Corpus (BTN Corpus), which contains 2 million Bangla sentences. Initially, Bangla written texts are tokenized, and each token is classified using a Temporal Convolutional Network (TCN) algorithm trained on the BTN Corpus to identify its semiotic class. This classification aids in generating normalized text, where the resulting tokens are reassembled into coherent sentences for final output. In addition to text normalization, we implement emotion classification for each normalized sentence using a Hierarchical Attention Network (HAN) model. The HAN model was trained on 67,277 normalized texts from the Bangla Normalized Emotion Text Corpus (BNET Corpus), categorizing each text into one of six emotion classes through a rule-based method with regular expressions. The proposed method demonstrates high accuracy rates, achieving a token classification accuracy of 99.977% with the TCN and an emotion classification accuracy of 99.735% with the HAN model.

关键词： Bangla Text Normalization Emotion Classification deep learning algorithms Expressive Text-to-Speech (ETTS) Synthesizer Large Bangla Text Corpus etc

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Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height

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Forest Ecosystems 2020年第2期7卷 141-158页

作者： ilker Ercanli Cankiri Karatekin University Faculty of Forestry18200ÇankırıTurkey

Background:deep learning algorithms(DLA)have become prominent as an application of Artificial Intelligence(Al)Techniques since *** paper introduces the DLA to predict the relationships between individual tree height(ITH)and the diameter at breast height(DBH).Methods:A set of 2024 pairs of individual height and diameter at breast height measurements,originating from 150 sample plots located in stands of even aged and pure Anatolian Crimean Pine(Pinus nigra *** ***(Lamb.)Holmboe)in Konya Forest *** present study primarily investigated the capability and usability of DLA models for predicting the relationships between the ITH and the DBH sampled from some stands with different growth *** 80 different DLA models,which involve different the alternatives for the numbers of hidden layers and neuron,have been trained and compared to determine optimum and best predictive DLAs network ***:It was determined that the DLA model with 9 layers and 100 neurons has been the best predictive network model compared as those by other different DLA,Artificial Neural Network,Nonlinear Regression and Nonlinear Mixed Effect *** alternative of 100#neurons and 9#hidden layers in deep learning algorithms resulted in best predictive ITH values with root mean squared error(RMSE,0.5575),percent of the root mean squared error(RMSE%,4.9504%),Akaike information criterion(AIC,-998.9540),Bayesian information criterion(BIC,884.6591),fit index(Fl,0.9436),average absolute error(AAE,0.4077),maximum absolute error(***,2.5106),Bias(0.0057)and percent Bias(Bias%,0.0502%).In addition,these predictive results with DLAs were further validated by the Equivalence tests that showed the DLA models successfully predicted the tree height in the independent ***:This study has emphasized the capability of the DLA models,novel artificial intelligence technique,for predicting the relationships between individual tree height and the diame

关键词： Artificial intelligence Prediction deep learning algorithms Individual tree height

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Machine learning and deep learning for clinical data and PET/SPECT imaging in Parkinson's disease: a review

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IET IMAGE PROCESSING 2020年第16期14卷 4013-4026页

作者： Khachnaoui, Hajer Mabrouk, Rostom Khlifa, Nawres Univ Tunis El Manar Inst Super Technol Med Tunis Lab Biophys & Technol Med Tunis 1006 Tunisia Bishops Univ Dept Comp Sci Sherbrooke PQ Canada

Machine learning (ML) is a subfield of Artificial Intelligence (AI) that is increasingly applied to several medical diagnosis tasks, including a wide range of diseases. Importantly, various ML models were developed to address the complexity of Parkinson's Disease (PD) diagnosis. PD is a neurodegenerative disease characterized by motor and non-motor disorders where its syndromes affect the daily lives of patients. Several Computer Aided Diagnosis and Detection (CADD) systems based on hand-crafted ML algorithms achieved promising results in distinguishing PD patients from Healthy Control (HC) subjects and other Parkinsonian syndrome categories using clinical data (e.g., speech and gait impairments) and medical imaging [e.g., Position Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT)]. Despite the good performance of hand-crafted ML algorithms, there is still a problem linked to the features' extraction and selection. In fact, deep learning DL has provided an ultimate solution for the features' extraction and selection related issue. An important number of studies on the diagnosis of PD using DL algorithms were developed recently. This study provides an overview of the application of hand-crafted ML algorithms and DL techniques for PD diagnosis. It also introduces key concepts for understanding the application of ML methods to diagnose PD.

关键词： learning (artificial intelligence) diseases positron emission tomography CAD single photon emission computed tomography medical diagnostic computing medical image processing neurophysiology machine learning clinical data Parkinson disease artificial intelligence medical diagnosis tasks ML models neurodegenerative disease nonmotor disorders computer-aided diagnosis detection systems hand-crafted ML algorithms single photon emission deep learning algorithms PD diagnosis ML methods Parkinsonian syndrome

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Detecting Elderly Behaviors Based on deep learning for Healthcare: Recent Advances, Methods, Real-World Applications and Challenges

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IEEE ACCESS 2022年 10卷 69802-69821页

作者： Almutairi, Mubarak Gabralla, Lubna A. Abubakar, Saidu Chiroma, Haruna Univ Hafr Al Batin Coll Comp Sci & Engn Hafar al Batin 31991 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Appl Coll Dept Comp Sci & Informat Technol Riyadh 11671 Saudi Arabia Abubakar Tafawa Balewa Univ Dept Math Sci Bauchi 740004 Nigeria

Machine learning has been applied in healthcare domain for the development of smart devices to improve the life of the elderly persons in the society. Taking care of elderly person in the society is a critical issue that need automation. To proffer solution, many researchers developed deep learning algorithms smart devices for detecting elderly behavior to improve the elderly healthcare. Despite the progress made in the applications of deep learning algorithms in elderly healthcare systems, to the best of the author's knowledge no comprehensive recent development has been published on this interesting research area especially focusing on deep learning. In this paper, we presented a comprehensive recent development on the advances, methods and real world applications on developing smart devices for detecting elderly behavior for use in smart home, smart clinic, smart hospital and smart elderly nursing home for elderly person's healthcare. Theories of the deep learning algorithms, recent development recorded as regard to the applicability of deep learning in elderly healthcare systems and case studies were discussed. A taxonomy based on the data extracted from the applicability of deep learning algorithms in elderly healthcare systems is created to ease pointing out areas that need more attention. The article shows that the deep learning algorithm that received tremendous attention from researchers is convolutional neural network architecture and its variants. To help in future development of the research area, we highlighted the challenges associated to the applicability of deep learning algorithms in elderly healthcare system and pointed out new point of view for future research. The research community can use our review as a benchmark for proposing novel deep learning algorithms based smart devices to detect elderly behavior for elderly healthcare systems. Industries and organizations can use the paper as a guide in selecting machine learning based smart device for

关键词： Older adults Medical services deep learning Smart devices Databases Search engines Machine learning algorithms Machine learning deep learning algorithms convolutional neural network elderly person behaviour VGG Internet of Things smart nursing home

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Reliable hybrid deep learning technique for an effective spectrum sensing in cognitive radio

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第6期44卷 10765-10779页

作者： Sivaranjani, S. Vivek, C. M Kumarasamy Coll Engn Dept Elect & Commun Engn Karur Tamilnadu India Sri Eshwar Coll Engn Dept Elect & Commun Engn Coimbatore Tamil Nadu India

Spectrum sensing will be an essential component in developing cognitive radio networks, which will be an essential component of the subsequent generation of wireless communication systems. Over the course of several decades, a great deal of different strategies, including cyclo-stationary, energy detectors, and matching filters, have been put up as potential solutions. Obviously, each of these methods comes with a few of negatives that you have to take into consideration. When the Signal-to-Noise Ratio (SNR) changes, energy detectors work poorly;cyclo-stationary detectors are technically sophisticated;and employing matching filters needs experience with Primary User (PU) signals. Researchers have recently been devoting a great deal of attention to Machine learning (ML) and deep learning (DL) algorithms as a result of the potential uses that these algorithms may have in the development of exceptionally accurate spectrum sensing models. The capacity to learn from data in a way that traditional learning algorithms are unable to has led to the rise in prominence of these types of algorithms. The Hybrid Model of Improved Long Short Term Memory with Improved Extreme learning Models (HILSTM-IELM), to be more specific, is what is being suggested since it reduces the amount of energy that is used during data transmission as well as the range and the duty cycle. Because of this, the disadvantage in existing methodology, proposed technique reduced to a certain level in energy consumption. In the last step of this analysis, the performance of the HILSTM-IELM-based spectrum sensing is compared to that of a variety of different methods that are currently in use. According to the findings of recent studies, the spectrum sensing method that was created provides superior performance to that of competing technologies in terms of the accuracy, sensitivity, and specificity of data transmission systems.

关键词： Improved long short-term memory improved extreme learning machines energy detectors cyclo-stationary features machine learning deep learning algorithms

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