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Examining Feasibility of Developing a Rock Mass classification for Hard Rock TBM Application Using Non-linear regression, regression tree and Generic Programming

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2018年第2期36卷 1145-1159页

作者： Salimi, A. Rostami, J. Moormann, C. Hassanpour, J. Univ Stuttgart Inst Geotech Engn Pfaffenwaldring 35 D-70569 Stuttgart Germany Colorado Sch Mines Earth Mech Inst Dept Min Engn 1600 Illinois St Golden CO 80401 USA Univ Tehran Sch Geol Coll Sci Tehran Iran

Geotechnical and geological parameters have the greatest impact on the performance of hard rock tunnel boring machines (TBMs). This includes the rock and rock mass properties that affect the rate of penetration (ROP) as well as the machine utilization that is heavily dependent on ground support type and related machine downtime and delays. However, despite the widespread use of TBMs and established track records, accurate estimation of machine performance is still a challenge, especially in complex geological conditions. The past studies have tried to use rock mass classification systems for improving the accuracy of the machine performance prediction. Rock mass classifications has been primarily developed for design of ground support, and as such, have not offered a good fit for estimation of TBM performance. This paper will review performance of a hard rock TBM in a 12.24 km long tunnel and offers analysis of field performance data to evaluate the relationship between various lithological units and TBM operation. The results of statistical analysis of the initial 5.83 km long tunnel indicate strong relationships between geomechanical parameters and TBM performance parameters. Site specific models, including Non-linear regression analysis (NLRA), classification and regression tree (CART), and Genetic Programming (GP) have been used for analysis of a TBM performance relative to the ground condition data. The current study has looked at the possibility of developing a new rock mass classification system for TBM application by using the above noted analysis. Preliminary results indicate that CART can be used for offering a proper rating scheme for a rock mass classification system that can be used for TBM applications.

关键词： TBM performance Penetration rate Non-linear regression analysis classification and regression tree Genetic programming

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Artificial Intelligence-Driven Approach to Optimizing Boiler Power Generation Efficiency: The Advanced Boiler Combustion Control Model

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ENERGIES 2025年第4期18卷

作者： Lee, Kyu-Jeong Choi, So-Won Lee, Eul-Bum Pohang Univ Sci & Technol POSTECH Grad Inst Ferrous & Eco Mat Technol Pohang 37673 South Korea Pohang Iron & Steel Co POSCO Investment & Engn Dept Energy Technol Sect Pohang 37754 South Korea Pohang Univ Sci & Technol POSTECH Dept Ind & Management Engn Pohang 37673 South Korea

The by-product gases generated during steel manufacturing processes, including blast furnace gas, coke oven gas, and Linz-Donawitz gas, exhibit considerable variability in composition and supply. Consequently, achieving stable combustion control of these gases is critical for improving boiler efficiency. This study developed the advanced boiler combustion control model (ABCCM) by combining the random forest (RF) and classification and regression tree (CART) algorithms to optimize the combustion of steam power boilers using steel by-product gases. The ABCCM derives optimal combustion patterns in real time using the RF algorithm and minimizes fuel consumption through the CART algorithm, thereby optimizing the overall gross heat rate. The results demonstrate that the ABCCM achieves a 0.86% improvement in combustion efficiency and a 1.7% increase in power generation efficiency compared to manual control methods. Moreover, the model reduces the gross heat rate by 58.3 kcal/kWh, which translates into an estimated annual energy cost saving of USD 89.6 K. These improvements contribute considerably to reducing carbon emissions, with the ABCCM being able to optimize fuel utilization and minimize excess air supply, thus enhancing the overall sustainability of steelmaking operations. This study underscores the potential of the ABCCM to extend beyond the steel industry.

关键词： steel mill steam power plant burners boiler combustion optimization combustion control by-product gases gross heat loss prediction advanced boiler combustion control model machine learning random forest classification and regression tree

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Impact of catchment and climate attributes on flood generating processes and their effect on flood statistics

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JOURNAL OF HYDROLOGY 2025年 646卷

作者： Fischer, Svenja Pahlow, Markus Singh, Shailesh Kumar Wageningen Univ & Res Hydrol & Environm Hydraul Wageningen Netherlands Univ Canterbury Dept Civil & Nat Resources Engn Christchurch New Zealand Natl Inst Water & Atmospher Res Christchurch New Zealand

A major source of uncertainty in flood statistics are the different flood generation processes. These make the assumption of homogeneous samples questionable. To overcome this issue, a framework for assessing the influence of catchment and climate attributes on flood-generating processes and their effect on flood statistics has been developed and applied to 252 catchments in New Zealand. Mean daily discharge data time series with a length ranging from 20 to 81 years were used. Flood events were classified according to their hydrograph shape. Three types were considered based on the different forcing: heavy rainfall of short duration (termed R1), moderate rainfall of medium intensity and duration (R2), and long-duration rainfall sequences of usually larger spatial extent (R3). The dominant flood type in each catchment was then linked to catchment and climate attributes. This allowed to identify the impact of each flood type on flood statistics and how the flood types have changed over time. The main drivers determining the flood type were rainfall variability and antecedent conditions. Small and steep catchments were dominated by heavy-rainfall floods of shorter duration, while flat and wet catchments were dominated by long-duration floods with larger volumes. Such information can support selection of effective flood protection and management measures.

关键词： Flood type Flood generation Flood statistics classification and regression tree Change-points

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Comprehensive Review of Privacy, Utility, and Fairness Offered by Synthetic Data

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IEEE ACCESS 2025年 13卷 15795-15811页

作者： Kiran, A. Rubini, P. Kumar, S. Saravana CMR Univ Dept CSE SOET Bengaluru 562149 Karnataka India CMR Univ PG Sch Dept IT SOET Bengaluru 562149 Karnataka India

Automation is the core transformation strategy that every industry wants to get on its roadmap today. Artificial Intelligence (AI) and Machine Learning (ML) are the key components of automation. It is increasingly used in both data analysis and building predictive models from the data. Growing privacy concerns, data confidentiality, and disclosure risks have posed a challenge to the accessibility of right and meaningful data. Several privacy-preserving and disclosure-limiting techniques have come up through research. One such disclosure limiting technique is Synthetic Data. Early research efforts have shown that synthetic data is an effective substitute for real data which can be effectively used to train AI and ML models. However, this needs a comprehensive evaluation before the data user can be confident enough that it is indeed a good substitute for real data. In this paper, we look at three main parameters of synthetic data which should provide a holistic assessment of the quality of synthetic data. First and foremost, how well synthetic data can preserve privacy and control disclosure, second is how good is its utility, and third, are they able to give fair results without any bias when used in machine learning. We review the existing literature to understand various disclosure control limiting methods, synthetic data generators, and then the validation methodologies and evaluation techniques. We understand how data privacy, utility and the fairness of synthetic data intervene with each other and identify the areas for future work.

关键词： Synthetic data Data privacy Privacy Machine learning Reviews Surveys Protection Differential privacy Uncertainty Imputation Artificial intelligence machine learning synthetic data statistical disclosure control differential privacy privacy enhancing technology classification and regression tree fairness SMOTE

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Estimating body weight in Sujiang pigs using artificial neural network, nearest neighbor, and CART algorithms: a comparative study using morphological measurements

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TROPICAL ANIMAL HEALTH AND PRODUCTION 2025年第1期57卷 1-15页

作者： Ergin, Malik Koskan, Ozgur Isparta Univ Appl Sci Fac Agr Dept Anim Sci Isparta Turkiye

The objectives of this study were to evaluate different machine learning algorithms for predicting body weight (BW) in Sujiang pigs using the following morphological traits: age, body length (BL), backfat thickness (BFT), chest circumference (CC), body height (BH), chest width (CW), and hip width (HW). Additionally, this study also investigated which machine learning algorithms could accurately and efficiently predict body weight in pigs using a limited set of morphological traits. For this purpose, morphological measurements of 365 mature (180 +/- 5 days) Sujiang pigs from the Jiangsu Sujiang Pig Breeding Farm in Taizhou, Jiangsu Province, China were used. The age of the pigs (180 +/- 5 days) was also included as a nominal predictor. In total, 218 individual measurements were obtained after data preprocessing. In the Sujiang pig dataset, BW had a significantly positive and high linear relationship with BH, BL, CW, HW, and CC resulting in values of 0.66, 0.72, 0.81, 0.84, and 0.88, respectively (p < 0.01). Artificial neural network (ANN), K-nearest neighbors (KNN), and classification and regression tree (CART) algorithms were used to predict BW. Overall, the ANN algorithm outperformed the other algorithms in this pig dataset according to the goodness of fit criteria of R-2 = 0.85, RMSE = 3.98, MAD = 3.25, MAPE = 4.25, SDR = 0.39, RAE = 0.002, MRAE = 0.008, and AIC = 97.96. The ANN algorithm was trained using several training algorithms, such as the Levenberg-Marquardt algorithm, Bayesian regularization, and scaled conjugate gradient. In addition, the number of neurons in the hidden layer was manipulated to 2, 3, or 4. All training algorithms yielded similar results. However, when the predictor variables were HW, BL, and BH, the Levenberg-Marquardt network had the best ability to predict body weight in Sujiang pigs (R-2 = 0.83). When BH measurements were not included in the model, the model's predictive ability decreased by approximately 5%. According to the results,

关键词： Artificial neural network K nearest neighbor classification and regression tree Sujiang pig Morphological measurements

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Machine Learning-Based classification and regression Approach for Early Detection of Large Break Loss-of-Coolant Accident Conditions

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年 1-21页

作者： Almomani, Belal Khuwaileh, Bassam A. Alam, Syed Bahauddin King Fahd Univ Petr & Minerals KFUPM Interdisciplinary Res Ctr Ind Nucl Energy IRC INE Dept Mech Engn Dhahran 31261 Saudi Arabia Univ Sharjah Dept Mech & Nucl Engn Sharjah 27272 U Arab Emirates Univ Illinois Talbot Lab Nucl Plasma & Radiol Engn MC-234104 South Wright StSuite 100 Urbana IL 61801 USA Univ Illinois Natl Ctr Supercomp Applicat 1205 W Clark St Urbana IL 61801 USA

Data-driven predictive approaches have significant potential for enhancing accident management and operator support in nuclear power plants (NPPs). Machine learning (ML)-based models can effectively monitor early signs of accidents and define initial conditions, allowing for better responses and reducing the need for manual intervention. This study explores using ML algorithms to identify the simulation-based initial conditions of accidents in a generic pressurized water reactor (GPWR), focusing on large loss-of-coolant accidents (LLOCAs) in primary cooling systems. Fuel temperature datasets were generated from the GPWR simulator for different LLOCA conditions, including 6 locations and break sizes of 0.11 m(2) to 0.22 m(2). The dataset includes 713,196 observations of fuel temperature from 12 zones of the reactor vessel for 36 LLOCA conditions within a 30-min interval. The classification and regression tree (CART) models were then used to predict the location and size of the break. Following post-learning and validation, CART model performed reasonably well with R-2 value of similar to 0.9 for break size prediction and a misclassification error of similar to 4% for break location. For performance evaluation, 12 new datasets with varying interpolated break sizes for the 6 locations were introduced, targeting the early detection during the time intervals of up to 10 min. The CART model in this study demonstrated acceptable performance in early identifying the break size and location of LLOCA conditions, with accuracy rates > similar to 70% and relative errors < similar to 6%. This approach aims to enhance emergency management and operator support to ensure timely responses and safe operations in NPPs.

关键词： classification and regression tree Loss of coolant Machine learning Nuclear power plant Pressurized water reactor

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Rural-Urban Differences in the Determinants of Subjective Well-Being Among X/Twitter Users in the United States

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POPULATION SPACE AND PLACE 2025年第1期31卷

作者： Jiang, Wenting Zhang, Mengxi Wu, Connor Y. H. Dong, Weichuan Oklahoma State Univ Dept Management Sci & Informat Syst Stillwater OK 74078 USA Virginia Tech Carilion Sch Med Dept Hlth Syst & Implementat Sci Roanoke VA USA Case Western Reserve Univ Sch Med Dept Populat & Quantitat Hlth Sci Cleveland Hts OH USA

Twitter Sentiment Geographical Index (TSGI) has been proposed to complement traditional surveys to measure subjective well-being (SWB) at the US county level. Our study aims to investigate determinants of TSGI-measured SWB in rural and urban US counties. Using the classification and regression tree, we identified phenotypes or county-level characteristics associated with high SWB. Counties with newer homes were the top characteristic of high SWB in both urban and rural areas. Counties of the identical phenotypes tend to concentrate geographically, with the most favorable phenotypes clustered in the South. Random Forest analysis identified additional characteristics of high SWB, including higher population density in rural areas and lower real estate tax ratio in urban areas. Our results yield a comprehensive understanding of determinants of SWB at the local level, guiding evidence-based policy decisions and community initiatives to improve well-being in target populations.

关键词： classification and regression tree random forest analysis social media SWB Twitter Sentiment Analysis

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classification and regression trees for Symbolic Data

Classification and Regression Trees for Symbolic Data

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作者： Zou, Wanxue University of Georgia

学位级别：Ph.D., Doctor of Philosophy

Compared to classical data which take a single value, there is another type of data, symbolic data, which can be a list, an interval, and even a distribution into consideration. Symbolic data are very common in our daily life; however, the analysis methods for symbolic data are very limited. For instance, a famous and useful method for supervised learning such as regression or classification is the decision tree. There are many useful algorithms based on the decision tree. However, the decision tree is only useful to classic data taking a single value, either numerical or categorical. In this dissertation, I will extend the classification and regression tree method (CART) to symbolic data.

关键词： classification and regression tree Decision tree Interval-valued data Modal multi-valued data Symbolic data

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A tree approach for variable selection and its random forest

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2025年 202卷

作者： Liu, Yu Qin, Xu Cai, Zhibo Univ Elect Sci & Technol China Sch Math Sci 2006 Xiyuan Ave Chengdu 611731 Sichuan Peoples R China Renmin Univ China Ctr Appl Stat 59 Zhongguancun St Beijing 100872 Peoples R China Renmin Univ China Sch Stat 59 Zhongguancun St Beijing 100872 Peoples R China

The Sure Independence Screening (SIS) provides a fast and efficient ranking for the importance of variables for ultra-high dimensional regressions. However, classical SIS cannot eliminate false importance in the ranking, which is exacerbated in nonparametric settings. To address this problem, a novel screening approach is proposed by partitioning the sample into subsets sequentially and creating a tree-like structure of sub-samples called SIS-tree. SIS-tree is straightforward to implement and can be integrated with various measures of dependence. Theoretical results are established to support this approach, including its "sure screening property". Additionally, SIS-tree is extended to a forest with improved performance. Through simulations, the proposed methods are demonstrated to have great improvement comparing with existing SIS methods. The selection of a cutoff for the screening is also investigated through theoretical justification and experimental study. As a direct application, classifications of high-dimensional data are considered, and it is found that the screening and cutoff can substantially improve the performance of existing classifiers.

关键词： Binary partition classification and regression tree Mutual information Random forests Sure independence screening

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Main characteristics of French farms adopting cereal-legume intercropping: A quantitative exploration at the national and local levels

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AGRICULTURAL SYSTEMS 2025年 223卷

作者： Yan, Elodie Martin, Philippe Carozzi, Marco Univ Paris Saclay INRAE AgroParisTech UMR SADAPT F-91120 Palaiseau France

CONTEXT: Cereal-legume intercropping is a diversification practice that offers many advantages, especially in low-input systems. However, its adoption remains low on European farms, as technical and economic barriers hinder its development. In recent years, an increase in the proportion of arable land cultivated with cereal- -legume intercrops has been observed in France. Three areas in particular - in Western, Eastern and Southern France - seem to be particularly dynamic. OBJECTIVE: This study aimed (i) to identify the main farm characteristics associated with the presence of cereal-legume intercrops at the national level in France and (ii) to highlight more specific characteristics that could explain the particular dynamics observed in each focus region. METHODS: We analysed data from the 2020 French Agricultural Census for 43,968 farms representative of the French arable crop, livestock, and mixed crop-livestock farming systems. Through a literature review, we identified key factors linked to the presence of cereal-legume intercrops and related them to 42 variables in the census. At the national level, the most important of these variables were identified and interpreted using a balanced random forest and a classification and regression tree (CART). We tested the CART obtained at the national level in the Western, Eastern, and Southern areas and conducted a random forest analysis for each area to identify local particularities. RESULTS AND CONCLUSION: At the national level, the presence of cereal-legume intercropping was strongly linked to organic farming and the presence of livestock, especially ruminants. These intercrops were prevalent on farms with high feed autonomy for the cattle and sheep. Additionally, they were commonly observed on farms with grain storage, possibly indicating feed autonomy, on-farm transformation, or marketing outside of agricultural cooperatives. The belonging to a farm machinery cooperative was also strongly associated with cereal-leg

关键词： Crop mixtures Crop diversification Farming systems Balanced random forest classification and regression tree

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