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Efficient neural network- and tree-based machine learning models for predicting shear capacity of RC slender walls

Asian Journal of Civil Engineering 2024年第4期25卷 3595-3609页

作者： Nguyen, Sy-Minh Tran, Ngoc-Long Nguyen, Trong-Ha Tran, Van-Binh Nguyen, Duy-Duan Department of Technology and Engineering Ha Tinh University Ha Tinh Viet Nam Department of Civil Engineering Vinh University Vinh 461010 Viet Nam

Slender reinforced concrete (RC) walls are popularly employed to improve the lateral loading capacity of high-rise buildings. Shear strength is an important target in designing RC walls subjected to horizontal loads such as earthquakes. This study aims to develop machine learning (ML) models for estimating the shear capacity of RC slender walls. A total of 154 test results, which were published in the literature, are gathered for training ML models. Two neural network-based models, i.e., Artificial neural network- Levenberg Marquardt (ANN-LM) and Artificial neural network-Bayesian regularization (ANN-BR), and two tree-based ML models including Random Forest (RF) and gradient boosting regression tree (GBRT), are developed to predict the shear strength of RC walls. The predicted results obtained from ML models are then compared with those from empirical formulas in design codes. It shows that ML models are superior in predicting the shear capacity of RC slender walls compared to other code-based models, especially, RF and GBRT highly efficient models. Moreover, a practical graphical user interface tool is proposed to simplify the practical design of RC slender walls. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Artificial neural network gradient boosting regression tree Graphical user interface Random Forest RC slender wall Shear strength

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Application of ensemble machine learning model in downscaling and projecting climate variables over different climate regions in Iran

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第12期29卷 17260-17279页

作者： Asadollah, Seyed Babak Haji Seyed Sharafati, Ahmad Shahid, Shamsuddin Islamic Azad Univ Dept Civil Engn Sci & Res Branch Tehran Iran Univ Teknol Malaysia Fac Engn Sch Civil Engn Johor Baharu 81310 Malaysia

This study evaluates the future climate fluctuations in Iran's eight major climate regions (G1-G8). Synoptic data for the period 1995-2014 was used as the reference for downscaling and estimation of possible alternation of precipitation, maximum and minimum temperature in three future periods, near future (2020-2040), middle future (2040-2060), and far future (2060-2080) for two shared socioeconomic pathways (SSP) scenarios, SSP119 and SSP245. The gradient boosting regression tree (GBRT) ensemble algorithm has been utilized to implement the downscaling model. Pearson's correlation coefficient (CC) was used to assess the ability of CMIP6 global climate models (GCMs) in replicating observed precipitation and temperature in different climate zones for the based period (1995-2014) to select the most suitable GCM for Iran. The suitability of 21 meteorological variables was evaluated to select the best combination of inputs to develop the GBRT downscaling model. The results revealed GFDL-ESM4 as the most suitable GCM for replicating the synoptic climate of Iran for the base period. Two variables, namely sea surface temperature (ts) and air temperature (tas), are the most suitable variable for developing a downscaling model for precipitation, while ts, tas, and geopotential height (zg) for maximum temperature, and tas, zg, and sea level pressure (psl) for minimum temperature. The GBRT showed significant improvement in downscaling GCM simulation compared to support vector regression, previously found as most suitable for the downscaling climate in Iran. The projected precipitation revealed the highest increase in arid and semi-arid regions (G1) by an average of 144%, while a declination in the margins of the Caspian Sea (G8) by -74%. The projected maximum temperature showed an increase up to +8 degrees C in highland climate regions. The minimum temperature revealed an increase up to +4 degrees C in the Zagros mountains and decreased by -4 degrees C in different climate zone

关键词： Global climate model Shared socioeconomic pathways scenarios gradient boosting regression tree Climate downscaling Climate change projections

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Machine learning based algorithms for wind pressure prediction of high-rise buildings

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ADVANCES IN STRUCTURAL ENGINEERING 2022年第10期25卷 2222-2233页

作者： Li, Yi Huang, Xuan Li, Yong-Gui Chen, Fu-Bin Li, Qiu-Sheng Hunan Univ Sci & Technol Hunan Prov Key Lab Struct Wind Resistance & Vibra Xiangtan Peoples R China Hunan Univ Sci & Technol Sch Civil Engn Taoyuan Rd Xiangtan 411201 Peoples R China Changsha Univ Sci & Technol Hunan Prov Key Lab Green Construct & Maintenance Changsha Peoples R China City Univ Hong Kong Dept Architecture & Civil Engn Kowloon Hong Kong Peoples R China

In recent years, machine learning (ML) techniques have been used in various fields of engineering practice. In order to evaluate the feasibility of machine learning algorithms for prediction of wind-induced effects on high-rise buildings, four ML algorithms including ridge regression, decision tree, random forest and gradient boosting regression tree are adopted in this study to predict wind pressures on Commonwealth Advisory Aeronautical Research Council standard tall building. The gradient boosting regression tree model is proved to be well performed in predicting both mean wind pressures and fluctuating wind pressures. Compared to expensive wind tunnel tests and time-consuming computational fluid dynamic simulations, it is expected that the gradient boosting regression tree model is an efficient and economical alternative for predicting wind pressures on high-rise buildings.

关键词： Wind pressure high-rise building machine learning gradient boosting regression tree predicting

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Determining leaf nutrient concentrations in citrus trees using UAV imagery and machine learning

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PRECISION AGRICULTURE 2022年第3期23卷 854-875页

作者： Costa, Lucas Kunwar, Sudip Ampatzidis, Yiannis Albrecht, Ute Univ Florida Southwest Florida Res & Educ Ctr Agr & Biol Engn Dept IFAS 2685 SR 29 North Immokalee FL 34142 USA Univ Florida Southwest Florida Res & Educ Ctr Hort Sci Dept IFAS 2685 SR 29 North Immokalee FL 34142 USA

Nutrient assessment of plants, a key aspect of agricultural crop management and varietal development programs, traditionally is time demanding and labor-intensive. This study proposes a novel methodology to determine leaf nutrient concentrations of citrus trees by using unmanned aerial vehicle (UAV) multispectral imagery and artificial intelligence (AI). The study was conducted in four different citrus field trials, located in Highlands County and in Polk County, Florida, USA. In each location, trials contained either 'Hamlin' or 'Valencia' sweet orange scion grafted on more than 30 different rootstocks. Leaves were collected and analyzed in the laboratory to determine macro- and micronutrient concentration using traditional chemical methods. Spectral data from tree canopies were obtained in five different bands (red, green, blue, red edge and near-infrared wavelengths) using a UAV equipped with a multispectral camera. The estimation model was developed using a gradient boosting regression tree and evaluated using several metrics including mean absolute percentage error (MAPE), root mean square error, MAPE-coefficient of variance (CV) ratio and difference plot. This novel model determined macronutrients (nitrogen, phosphorus, potassium, magnesium, calcium and sulfur) with high precision (less than 9% and 17% average error for the 'Hamlin' and 'Valencia' trials, respectively) and micro-nutrients with moderate precision (less than 16% and 30% average error for 'Hamlin' and 'Valencia' trials, respectively). Overall, this UAV- and AI-based methodology was efficient to determine nutrient concentrations and generate nutrient maps in commercial citrus orchards and could be applied to other crop species.

关键词： Artificial intelligence gradient boosting regression tree Remote sensing UAV

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Data-driven approach for air pollutant concentrations forecasting: A window-based multi-output GBRT approach

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ATMOSPHERIC RESEARCH 2024年 307卷

作者： Du, Shikang Chen, Siyu Cheng, Shanling He, Jiaqi Liu, Chuwei Lian, Lulu Zhang, Chao Zhao, Dan Yin, Nan Guan, Yawen Lanzhou Univ Coll Earth & Environm Sci Lanzhou 730000 Gansu Peoples R China Lanzhou Univ Coll Atmospher Sci Lanzhou 730000 Gansu Peoples R China Lanzhou Univ Minist Educ Key Lab Semiarid Climate Change Lanzhou 730000 Gansu Peoples R China

Air pollutant concentrations forecasting is essential for detrimental public exposure prevention, air quality enhancement, and effective environmental policy shaping. Recently, air pollutant concentrations forecasting based on machine learning has been considerably advanced with the development of data-driven approaches. However, multi-source data constraints and architectural complexity in machine learning approaches limit the use of machine learning in actual scenarios. Herein we present a window-based multi-output gradient boosting regression tree (WM-GBRT) approach to predict air pollutant concentrations for the next 48 h at each monitoring station, considering air pollutant monitoring data, meteorological monitoring data, NECP-GFS data, and GEOSCF data. The key innovation of our approach lies in the flexible input window configuration and the implementation of an multi-step independent strategy. The experiments were conducted in China and compared to existing data-driven baselines, we show that our approach achieved the superior performance in forecasting concentrations of six air pollutants, with a considerable improvement of 6.94 % (PM2.5), 12.3 % (PM10), 3.84 % (O3), 30.6 % (SO2), 9.84 % (NO2), and 93.9 % (CO) when compared to the second-best baselines. This simple yet efficient approach can be easily applied, providing a reliable technical method for predicting air pollutant concentrations.

关键词： Air pollutant forecasting Chemical transport model Time series forecasting gradient boosting regression tree Machine learning

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Continuous blood pressure monitoring using photoplethysmography and electrocardiogram signals by random forest feature selection and GWO-GBRT prediction model

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartC期88卷

作者： Liu, Shimin Huang, Zhiwen Zhu, Jianmin Liu, Baolin Zhou, Panyu Univ Shanghai Sci & Technol Sch Mech Engn Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Sch Hlth Sci & Engn Shanghai 200093 Peoples R China Second Mil Med Univ Naval Med Univ Changhai Hosp Dept Orthoped Shanghai 200433 Peoples R China

Blood pressure (BP) is an important indicator of a person's cardiovascular status, but accurate, continuous BP monitoring remains a challenge. To improve the monitoring performance of continuous BP using electrocardiogram (ECG) and photoplethysmography (PPG) signals, a continuous BP monitoring method based on random forest feature selection (RFFS) and a gray wolf optimization-gradient boosting regression tree (GWO-GBRT) prediction model is developed in this study. First, features in time, frequency, and time-frequency domains are extracted from PPG and ECG signals to supply comprehensive characteristics for BP monitoring. Then, the RFFS method is employed to select sensitive features highly correlated with BP from the candidate feature sets, which reduced redundant information and improved monitoring efficiency. Next, a hybrid prediction model of the gray wolf optimization (GWO) technique and gradient boosting regression tree (GBRT) algorithm is established to learn the dependency relationship between BP and sensitive features. And a new fitness function of GWO is designed to balance the monitoring accuracy and consistency. Finally, ablation and comparative experiments demonstrate the effectiveness and advancement of the proposed method, using the ECG and PPG signals of 150 people downloaded from the MIMIC-III database. The mean absolute error (MAE) and standard deviation (STD) of the proposed method in predicting systolic blood pressure were 2.91 mmHg and 3.97 mmHg, and those of diastolic blood pressure were 1.71 mmHg and 2.35 mmHg. Its monitoring accuracy has surpassed the Association for Advancement of Medical Instrumentation (AAMI) standard and reached the "A" level standard of the British Hypertension Society (BHS) protocol.

关键词： Blood pressure monitoring Electrocardiography Photoplethysmography gradient boosting regression tree Grey wolf optimization

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New data-driven estimation of metal element in rocks using a hyperspectral data and geochemical data

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ORE GEOLOGY REVIEWS 2024年 165卷

作者： Ma, Xiumei Wang, Jinlin Zhou, Kefa Zhang, Wenqiang Zhang, Zhixing De Maeyer, Philippe Van de Voorde, Tim Xinjiang Inst Ecol & Geog Chinese Acad Sci State Key Lab Desert & Oasis Ecol Urumqi 830011 Peoples R China Xinjiang Key Lab Mineral Resources & Digital Geol Urumqi 830011 Peoples R China Chinese Acad Sci Xinjiang Res Ctr Mineral Resources Urumqi 830011 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Ghent B-9000 Ghent Belgium Sino Belgian Joint Lab Geoinformat B-9000 Ghent Belgium Sino Belgian Joint Lab Geoinformat Urumqi 830011 Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Beijing 10094 Peoples R China Chinese Acad Sci Xinjiang Inst Ecol & Geog Xinjiang Res Ctr Mineral Resources Urumqi 830011 Peoples R China

The inversion of Cu contents in rocks based on hyperspectral remote sensing is indicative of geochemical exploration as well as mineral resource exploration. Due to the strong redundancy of band information in hyperspectral data, characteristic band selection is the key to improving the accuracy of metal element inversion models in hyperspectral data. In this study, we proposed a framework that innovatively focuses on secondary screening method (SSM) as the core, allowing users to add different data preprocessing methods and machine learning algorithms to the framework based on expert experience, to obtain high-precision metal element inversion models suitable for sampling data. we first preprocessed the measured spectral curves using the first order derivative, second order derivative, continuum removal, multiple scattering correction and variance analysis to highlight band absorption characteristics. In addition, we performed SSM to obtain Cu-related characteristic bands, which were further used as input data to train and compare the performance of Partial Least Square regression (PLSR), Support Vector regression (SVR), Multi-Layer Perceptron (MLP), Random Forest (RF) and gradient boosting regression tree (GBRT). The results show that both the spectral pretreatment and modeling method could potentially impact the inversion accuracy. The accuracy of the ensemble method applied in fresh faces data (i.e., a CR-SSM-RF with R2 values of 0.77 and CR-SSM-GBRT with R2 values of 0.81) performed better than other models with an accuracy improvement of up to 80%. Compared to the full band model, the accuracy of characteristic bands selection model increased 50%. This also illustrates that the CR-SSM-RF or CRSSM-GBRT model significantly improves the accuracy of the inversion of Cu contents in rocks compared to the original spectral reflectance. Our study provides a new idea for selecting different spectral preprocessing methods as the input values for the future model and wil

关键词： Hyperspectral remote sensing Cu element Continuum removal method gradient boosting regression tree Inversion accuracy

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Stranded asset risk assessment on ship investments

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TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 2023年 124卷

作者： Jeong, Hoejin Yun, Heesung Son, Junho Korea Maritime & Ocean Univ Dept Shipping Management 727 Taejong Ro Pusan 49112 South Korea Korea Maritime & Ocean Univ Grad Sch Maritime Finance 727 Taejong Ro Pusan 49112 South Korea Korea Maritime & Ocean Univ Dept Comp Engn 727 Taejong Ro Busan 49112 South Korea

The transition towards a low-carbon economy is crucial to achieve the Paris climate goals, but it also presents the risk of stranded assets across various industries. However, existing research has mainly focused on upstream and power sectors, leaving the stranded asset risk of individual assets less understood. This study proposes a real options-based framework for evaluating the stranding risk of carbon-intensive assets. To illustrate its practical application, we apply the framework to a fossil-fuel-powered bulk carrier. The results show that under carbon pricing, the shipowner should expect to bear approximately US$26.5 million of stranded asset risk, which would decrease to US$25.2 million if the ship reduces speed to comply with carbon intensity regulations. We conclude that our framework will enable an adequate assessment of these risks, leading to more practical implications for investment decision-making and supporting climate risk management.

关键词： Bulk carrier gradient boosting regression tree Least -squares Monte Carlo Real options Stranded assets

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Analysis of the relationship between metro ridership and built environment: A machine learning method considering combinational features

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2024年 144卷

作者： Li, Linchao Zhong, Liangjian Ran, Bin Du, Bowen Shenzhen Univ Coll Civil & Transportat Engn Shenzhen Peoples R China Univ Wisconsin Madison Dept Civil & Environm Engn Madison WI USA Beihang Univ Sch Comp Sci Beijing Peoples R China

Limited studies have examined the relationship between combinational features of the built environment and metro ridership. In this study, we applied the gradient boosting regression tree (GBRT) to explore the non-linearity effects for metro commuter ridership and non-commuter ridership, respectively. The results show that the number of bus stops is an important predictor of urban metro commuter ridership, whereas residential and community services, road density, and leisure and entertainment are important predictors of urban metro non-commuter ridership. Based on the partial dependence plots generated by GBRT, the results confirm that combinational features of the built environment have a larger impact on commuter ridership (e.g., high-high level road density-life services on the destination side). Combinational features at a higher level exhibit a more substantial influence on commuter ridership, whereas combinational features at a lower level demonstrate a greater impact on non-commuter ridership.

关键词： Metro Built environment Combinational features Nonlinear interaction gradient boosting regression tree

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Estimating the urban atmospheric boundary layer height from remote sensing applying machine learning techniques

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ATMOSPHERIC RESEARCH 2022年 266卷

作者： Moreira, Gregori de Arruda Sanchez-Hernandez, Guadalupe Guerrero-Rascado, Juan Luis Cazorla, Alberto Alados-Arboledas, Lucas Andalusian Inst Earth Syst Res IISTA CEAMA Granada Spain Univ Granada Dept Appl Phys Granada Spain Fed Inst Sao Paulo IFSP Sao Paulo Brazil Univ Jaen Dept Phys Jaen 2371 Spain

This study proposes a new methodology to estimate the Atmospheric Boundary Layer Height (ABLH), discriminating between Convective Boundary Layer and Stable Boundary Layer heights, based on the machine learning algorithm known as gradient boosting regression tree. The algorithm proposed here uses a first estimation of the ABLH derived applying the gradient method to a ceilometer signal and several meteorological variables to obtain ABLH values comparable to those derived from a microwave radiometer. A deep analysis of the model configuration and its inputs has been performed in order to avoid the model overfitting and ensure its applicability. The hourly and seasonal values and variability of the ABLH values obtained with the new algorithm have been analyzed and compared with the initial estimations obtained using only the ceilometer signal. Mean Relative Errors (MRE) between the ABLH estimated with the new algorithm and microwave radiometer show a daily pattern with their highest values during the night-time (stable situations) and their lowest values along the day-time (convective situations). This pattern has been observed for all the seasons with MRE ranging between-5% and 35%. This result notably improves those ABLH values derived by applying the gradient method to ceilometer data during convective situations and enables the Stable Boundary Layer height detection at night and early morning, instead of only Residual Layer top height. Finally, the model performance has been directly validated in three particular cases: clear-sky day, presence of low-clouds and dust outbreak event. In these three particular situations, ABLH values obtained with the new algorithm follow the pattern obtained with the microwave radiometer presenting very similar values, thus confirming the good model performance. In this way it is feasible by the combination of the proposed method with gradient method, to estimate Convective, Stable and Residual Boundary Layer height from ceilometer d

关键词： Atmospheric Boundary Layer height Ceilometer gradient boosting regression tree

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