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Optimization-based stacked machine-learning method for seismic probability and risk assessment of reinforced concrete shear walls

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartD期255卷

作者： Kazemi, Farzin Asgarkhani, Neda Jankowski, Robert Gdansk Univ Technol Fac Civil & Environm Engn ul Narutowicza 11-12 PL-80233 Gdansk Poland Univ Naples Federico II Sch Polytech & Basic Sci Dept Struct Engn & Architecture Naples Italy

Efficient seismic risk assessment aids decision-makers in formulating citywide risk mitigation plans, providing insights into building performance and retrofitting costs. The complexity of modeling, analysis, and postprocessing of the results makes it hard to fast-track the seismic probabilities, and there is a need to optimize the computational time. This research addresses seismic probability and risk assessment of reinforced concrete shear walls (RCSWs) by introducing stacked machine learning (Stacked ML) models based on Bayesian optimization (BO), genetic algorithm (GA), particle swarm optimization (PSO), and gradient-based optimization (GBO) algorithms. The study investigates 4-, to 15-Story RCSWs assuming different bay lengths and soil types to build a comprehensive database based on the incremental dynamic analysis (IDA) subjected to 56 near-field pulse-like and no-pulse records. Having 227,200 and 63,384 data points for a median of IDA curve (MIDA) and seismic probability curve, respectively, the proposed Stacked ML models have shown good performance on curve fitting ability by accuracy of 99.1% and 99.4% for MIDA and seismic fragility curves, respectively. In addition, the proposed models can estimate the mean annual frequency, lambda, which is a key parameter in seismic risk assessment of buildings. To provide the results of the study for general buildings, a user-friendly GUI is proposed that facilitates result utilization, offering insights into seismic performance levels, providing the estimated MIDA and seismic failure probability curves, and mean annual frequency calculations for specific performance levels and seismic hazard curves.

关键词： Reinforced concrete shear wall machine learning algorithm Seismic failure probability Optimization algorithm Seismic performance curve Seismic retrofit

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machine learning method as a tool to estimate the vibrations of the concrete structures reinforced by advanced nanocomposites

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2025年第5期32卷 777-793页

作者： Lin, Yongqing Ibraheem, Awad A. Beijing Jiaotong Vocat Tech Coll Beijing Peoples R China King Khalid Univ Phys Dept Abha Saudi Arabia

The integration of advanced nanocomposites into concrete structures presents a promising avenue for enhancing their mechanical properties and durability. However, accurately predicting the vibrations of such structures remains a complex and challenging task due to the nonlinear behavior of the materials involved and the intricate interplay of various influencing factors. In this study, we propose the application of machine learning techniques as a powerful tool for estimating the vibrations of concrete structures reinforced with advanced nanocomposites. By leveraging the vast amounts of data generated from mathematical modeling, machine learning algorithms can effectively capture the intricate relationships between material properties, structural configurations, environmental conditions, and vibration responses. This article provides mathematical modeling simulation as the input of machine learning methods suitable for vibration prediction in concrete structures, including artificial neural networks. Furthermore, we discuss the key considerations and challenges associated with developing accurate and reliable machine learning models for this specific application domain, such as feature selection, model complexity, data quality, and interpretability. Through a comprehensive mathematical modeling, we highlight the potential of machine learning as a versatile and efficient tool for predicting vibrations in concrete structures reinforced with graphene oxide powders (GOPs). The integration of machine learning into the design, analysis, and maintenance of such structures not only facilitates more accurate predictions of vibration behavior but also enables proactive decision-making and optimization of structural performance. Finally, we outline future research directions and opportunities for further advancing the application of machine learning in the field of structural engineering and nanocomposite materials.

关键词： Advanced nanocomposites concrete structures machine learning algorithm graphene oxide powders reinforcement vibrations

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Synthesis and characterization of novel lignocellulosic biomass-derived activated carbon for dye removal: machine learning optimization, mechanisms, and antibacterial properties

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BIOMASS & BIOENERGY 2025年 192卷

作者： Mohammadpour, Amin Dolatabadi, Maryam Bontempi, Elza Shahsavani, Ebrahim Jahrom Univ Med Sci Res Ctr Social Determinants Hlth Jahrom Iran Kerman Univ Med Sci Environm Hlth Engn Res Ctr Kerman Iran Univ Brescia Dept Mech & Ind Engn Via Branze 38 I-25123 Brescia Italy Natl Interuniv Consortium Mat Sci & Technol INSTM Florence Italy

Transforming waste materials into valuable products plays a crucial role in promoting sustainability and protecting environment. In this study, activated carbon derived from sugarcane bagasse, a lignocellulosic biomass source, was coated with iron and manganese for the adsorption and photodegradation of Acid Black 1 (AB1) dye from aqueous solutions. The effects of various parameters were investigated using Central Composite Design (CCD) and a Multi-Layer Perceptron (MLP) algorithm. The results of the characterization indicated that the iron and manganese particles were uniformly dispersed on the activated carbon. While CCD determined ideal parameters to be an initial concentration of 20.15 mg L- 1, a dose of 25 mg/30 mL, a pH of 5, and a time of 40 min, the MLP algorithm proposed slightly different conditions: an initial concentration of 26.66 mg L- 1, dose of 25 mg, pH of 5.0, and a time of 25.05 min. Despite these differences, both methods projected impressive AB1 removal efficiency, 100 % for CCD and 99.02 % for MLP, underscoring the potential effectiveness of these strategies in AB1 mitigation. Concentration emerged as the predominant factor influencing the removal process, as determined by the MLP algorithm. The results showed that the major active species in the degradation of AB1 were ecb - and center dot O2- , while hvb + species also participated to some extent, and triethanolamine (TEOA) had a minor effect on the degradation efficiency. The nanocomposite exhibited a high antibacterial activity against Staphylococcus aureus, resulting in a large zone of inhibition. The optimized nanocomposite could be used as an effective nanomaterial to remove hazardous contaminants.

关键词： Lignocellulosic biomass Modified activated carbon Nanocomposite Dye Optimization process machine learning algorithm Removal

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An automatic model selection-based machine learning approach to predict seawater intrusion into coastal aquifers

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ENVIRONMENTAL EARTH SCIENCES 2024年第9期83卷 287-287页

作者： Roy, Dilip Kumar Paul, Chitra Rani Munmun, Tasnia Hossain Datta, Bithin Bangladesh Agr Res Inst Irrigat & Water Management Div Gazipur 1701 Bangladesh James Cook Univ Coll Sci & Engn Discipline Civil Engn Townsville Qld 4811 Australia

Concerns about seawater intrusion resulting from unplanned mining of groundwater from coastal aquifers have become a global issue. To address this, it is crucial to implement a well-structured plan for groundwater withdrawal that can effectively manage and restrict salinity levels within the aquifer to acceptable levels. An integrated simulation-optimization (S-O) system has been a suitable tool for developing an optimum groundwater withdrawal scheme for managing seawater intrusion. The success of an S-O methodology largely depends on the use of computationally competent surrogates for the intricate simulation model. Although several surrogate models have been recently proposed to create management models for seawater intrusion using integrated S-O approach, the majority of these surrogates have been developed based on the subjective judgement. To fill this research gap, this study proposes an automatic model selection (AutoML)-based machine learning (ML) approach to predict the mechanisms of seawater intrusion in coastal aquifers. The AutoML was performed by optimizing the hyperparameters of a number of ML algorithms and selecting the best performing algorithm utilizing the asynchronous successive halving algorithm (ASHA). The best performing models were developed at 16 monitoring locations (MLs) using the predictor-response training data originated from a simulation model. Results revealed the capability of the ASHA optimization-based AutoML scheme to optimally select the best performing models with adequate prediction accuracies for the particular MLs. The selected best models at various monitoring locations exhibited high performance with accuracy (= 1), R (similar to 0.99), NS (similar to 0.99), IOA (similar to 0.99), and KGE (similar to 0.99), which are close to 1, indicating excellent model accuracy. Furthermore, the models demonstrated an RMSE value range of 0.0003-1.4987 mg/L, a relatively small range that is considered favorable for any predictive modeling

关键词： Coastal aquifer Seawater intrusion Automatic model selection machine learning algorithm Integrated simulation-optimization

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Flexible Multimodal Magnetoresistive Sensors Based on Alginate/Poly(vinyl alcohol) Foam with Stimulus Discriminability for Soft Electronics Using machine learning

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ACS APPLIED MATERIALS & INTERFACES 2024年第16期16卷 21118-21132页

作者： Fu, Yu Wang, Shuangkun Wang, Dong Tian, Ye Ban, Xinxing Wang, Xing Zhao, Zhihua Wan, Zhenshuai Wei, Ronghan Henan Univ Technol Henan Key Lab Superhard Abras & Grinding Equipment Zhengzhou 450001 Peoples R China Henan Univ Technol Sch Mech & Elect Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Mech & Safety Engn Zhengzhou 450001 Peoples R China

Flexible foam-based sensors have attracted substantial interest due to their high specific surface area, light weight, superior deformability, and ease of manufacture. However, it is still a challenge to integrate multimodal stimuli-responsiveness, high sensitivity, reliable stability, and good biocompatibility into a single foam sensor. To achieve this, a magnetoresistive foam sensor was fabricated by an in situ freezing-polymerization strategy based on the interpenetrating networks of sodium alginate, poly(vinyl alcohol) in conjunction with glycerol, and physical reinforcement of core-shell bidisperse magnetic particles. The assembled sensor exhibited preferable magnetic/strain-sensing capability (GF approximate to 0.41 T-1 for magnetic field, 4.305 for tension, -0.735 for bending, and -1.345 for pressing), quick response time, and reliable durability up to 6000 cycles under external stimuli. Importantly, a machine learning algorithm was developed to identify the encryption information, enabling high recognition accuracies of 99.22% and 99.34%. Moreover, they could be employed as health systems to detect human physiological motion and integrated as smart sensor arrays to perceive external pressure/magnetic field distributions. This work provides a simple and ecofriendly strategy to fabricate biocompatible foam-based multimodal sensors with potential applications in next-generation soft electronics.

关键词： alginate/poly(vinyl alcohol) foam multimodal sensor responsiveness machine learning algorithm soft electronics

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Thermal deformation behavior investigation of Ti-10V-5Al-2.5fe-0.1B titanium alloy based on phenomenological constitutive models and a machine learning method

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JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T 2024年 29卷 589-608页

作者： Zhang, Shuai Zhang, Haoyu Liu, Xuejia Wang, Shengyuan Wang, Chuan Zhou, Ge Zhang, Siqian Chen, Lijia Shenyang Univ Technol Sch Mat Sci & Engn Shenyang 110870 Liaoning Peoples R China Shenyang Univ Technol Shenyang Key Lab Adv Struct Mat & Applicat Shenyang 110870 Liaoning Peoples R China Liaoning Univ Coll Light Ind Shenyang 110036 Liaoning Peoples R China

The two-phase titanium alloy Ti-10 V-5Al-2.5Fe-0.1 B was taken as the experimental material, and thermal compression experiments were carried out at a deformation temperature of 770-920 degrees C and a strain rate of 0.0005-0.5 s(-1). An Arrhenius model, a modified Johnson -Cook model, and an improved BP neural network model based on the sparrow search algorithm (SSA -BP) model were established to predict the high temperature rheological stress of the alloy. A comparison of the prediction accuracy of the three models was made. When the partial random data in the rheological curves was used for model building and relatively independent data were used for predicting the rheological stress, the SSA -BP model had higher prediction accuracy, which exhibits the highest mean square correlation coefficient (R-2) value of 0.9992 and the lowest root mean square error (RMSE) and average absolute relative error (AARE) values of 1.3031, and 2.0947 %, respectively. The ability of three models to predict the rheological stress for the new process parameters was verified. Results show that the SSABP model still has better prediction ability, which exhibits the highest mean square correlation coefficient (R-2) value of 0.9720 and the lowest root mean square error (RMSE) and average absolute relative error (AARE) values of 5.0099, and 6.0382 %, respectively. The predicted values of SSA -BP for the rheological stress were used to construct the hot processing map. Results show that the trend of the power dissipation factor (eta) value from the hot processing map predicted by SSA -BP can well agree with the microstructure evolution of the alloy.

关键词： Two-phase titanium alloys Thermal deformation Phenomenological constitutive model machine learning algorithm Hot processing map

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Uncovering glycolysis-driven molecular subtypes in diabetic nephropathy: a WGCNA and machine learning approach for diagnostic precision

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BIOLOGY DIRECT 2025年第1期20卷 1-16页

作者： Fan, Chenglong Yang, Guanglin Li, Cheng Cheng, Jiwen Chen, Shaohua Mi, Hua Guangxi Med Univ Affiliated Hosp 1 Dept Urol Nanning 530021 Guangxi Peoples R China Guangxi Med Univ Canc Hosp Dept Urol Nanning 530000 Guangxi Peoples R China

IntroductionDiabetic nephropathy (DN) is a common diabetes-related complication with unclear underlying pathological mechanisms. Although recent studies have linked glycolysis to various pathological states, its role in DN remains largely *** this study, the expression patterns of glycolysis-related genes (GRGs) were first analyzed using the GSE30122, GSE30528, and GSE96804 datasets, followed by an evaluation of the immune landscape in DN. An unsupervised consensus clustering of DN samples from the same dataset was conducted based on differentially expressed GRGs. The hub genes associated with DN and glycolysis-related clusters were identified via weighted gene co-expression network analysis (WGCNA) and machine learning algorithms. Finally, the expression patterns of these hub genes were validated using single-cell sequencing data and quantitative real-time polymerase chain reaction (qRT-PCR).ResultsEleven GRGs showed abnormal expression in DN samples, leading to the identification of two distinct glycolysis clusters, each with its own immune profile and functional pathways. The analysis of the GSE142153 dataset showed that these clusters had specific immune characteristics. Furthermore, the Extreme Gradient Boosting (XGB) model was the most effective in diagnosing DN. The five most significant variables, including GATM, PCBD1, F11, HRSP12, and G6PC, were identified as hub genes for further investigation. Single-cell sequencing data showed that the hub genes were predominantly expressed in proximal tubular epithelial cells. In vitro experiments confirmed the expression pattern in *** study provides valuable insights into the molecular mechanisms underlying DN, highlighting the involvement of GRGs and immune cell infiltration.

关键词： Diabetic nephropathy Glycolysis Glycolysis-related genes machine learning algorithm Hub genes

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Enhancing digital health services: A machine learning approach to personalized exercise goal setting

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DIGITAL HEALTH 2024年 10卷

作者： Fang, Ji Lee, Vincent C. S. Ji, Hao Wang, Haiyan Southeast Univ Tranportat Coll Southeast Univ Nanjing 211189 Peoples R China Monash Univ Dept Data Sci & Artificial Intelligence Melbourne Australia Hangzhou Med Coll Hangzhou Peoples R China

Background The utilization of digital health has increased recently, and these services provide extensive guidance to encourage users to exercise frequently by setting daily exercise goals to promote a healthy lifestyle. These comprehensive guides evolved from the consideration of various personalized behavioral factors. Nevertheless, existing approaches frequently neglect the users' dynamic behavior and the changing in their health *** This study aims to fill this gap by developing a machine learning algorithm that dynamically updates auto-suggestion exercise goals using retrospective data and realistic behavior *** We conducted a methodological study by designing a deep reinforcement learning algorithm to evaluate exercise performance, considering fitness-fatigue effects. The deep reinforcement learning algorithm combines deep learning techniques to analyze time series data and infer user's exercise behavior. In addition, we use the asynchronous advantage actor-critic algorithm for reinforcement learning to determine the optimal exercise intensity through exploration and exploitation. The personalized exercise data and biometric data used in this study were collected from publicly available datasets, encompassing walking, sports logs, and *** In our study, we conducted the statistical analyses/inferential tests to compare the effectiveness of machine learning approach in exercise goal setting across different exercise goal-setting strategies. The 95% confidence intervals demonstrated the robustness of these findings, emphasizing the superior outcomes of the machine learning *** Our study demonstrates the adaptability of machine learning algorithm to users' exercise preferences and behaviors in exercise goal setting, emphasizing the substantial influence of goal design on service effectiveness.

关键词： Digital health service exercise goal setting changing exercise behavior machine learning algorithm internet of things technologies fitness-fatigue effect exercise performance

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Forecasting personal heat strain under extremely hot environments: Utilizing feature importance in machine learning

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartE期133卷

作者： Seo, Seungwon Choi, Yujin Koo, Choongwan Incheon Natl Univ Div Architecture & Urban Design Incheon 22012 South Korea

Since the frequency and intensity of heatwaves are expected to increase due to global warming, it is crucial to establish a simplified approach to preventing personal heat-related illnesses as occupational hazard under extremely hot environments, by taking into account individual differences in heat strain. In light of this, this study proposed a simplified forecast model for personal heat strain under extremely hot environments by utilizing feature importance in machine learning, focused on enhancing its field applicability. Using the 1417 records gathered from the experiments under extremely hot conditions, the simplified model was developed by utilizing four types of machine learning algorithms (i.e., random forest, extreme gradient boosting, support vector regression, and multi-layer perceptron). As a result, the proposed simplified models with four principal features (i.e., dry-bulb temperature, radiant temperature, relative humidity, and percentage body fat) were found to be the most reliable, resulting in a small difference of 0.047 degrees C compared to the reference model. Since the principal biometric features can be easily measured by a simple and low-cost body composition test (but not with the physical measurement of personal core body temperature) before they are put into the field, the proposed approach will aid in providing more systematic and efficient management system for proactively preventing personal heat-related illnesses. Furthermore, it is expected that the field applicability of the proposed approach can be continuously enhanced by utilizing the accumulated big data gathered from the field-based living-lab projects over a lengthy period.

关键词： Personal heat strain Extremely hot environments Forecasting model Principal biometric features machine learning algorithm Field applicability

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A machine learning-based forecasting model for personal maximum allowable exposure time under extremely hot environments

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SUSTAINABLE CITIES AND SOCIETY 2024年 101卷

作者： Choi, Yujin Seo, Seungwon Lee, Junsoo Kim, Tae Wan Koo, Choongwan Incheon Natl Univ Div Architecture & Urban Design Incheon 22012 South Korea

As global warming leads to an increase in the frequency and intensity of heatwaves, protecting outdoor workers from heat-related illnesses becomes paramount. To address this challenge, this study aimed to forecast personal maximum allowable exposure time by considering individual differences under extremely hot environments. To enhance the prediction accuracy of the proposed approach, a machine learning-based error correction model was developed in conjunction with the PHS_HR method (i.e., predicted heat strain index using real-time heart rate), reflecting dynamic changes in personal biometric characteristics (e.g., heart rate, body fat percentage, etc.) as well as environmental conditions and exposure time. Among the developed models, the multi-layer perceptron (MLP) algorithm demonstrated a high degree of reliability in forecasting personal maximum allowable exposure time, achieving a mean absolute error (MAE) of 0.19 minutes (11 seconds), compared to the existing PHS_HR method (MAE: 5.05 minutes). This study highlights the potential benefits of data-driven computational methods in occupational health and safety management. By providing more accurate predictions of personal maximum allowable exposure time under extremely hot environments, the risk of heat-related illnesses outdoors can be effectively mitigated. As heatwaves become more prevalent, the proposed approach offers a more valuable tool to ensure safer work environments.

关键词： Maximum allowable exposure time extremely hot environment construction workers continuous work time predicted heat strain machine learning algorithm

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