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Spatial prediction of soil contamination based on machine learning: a review

《环境科学与工程前沿（英文）》 2023年 第17卷 第8期 doi: 10.1007/s11783-023-1693-1

摘要：

● A review of machine learning (ML) for spatial prediction of soil contamination.

关键词： Soil contamination     Machine learning     Prediction     Spatial distribution    

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Advancing agriculture with machine learning: a new frontier in weed management

《农业科学与工程前沿（英文）》 doi: 10.15302/J-FASE-2024564

摘要：

● Machine learning offers innovative and sustainable weed management approaches.

关键词： Weed management     herbicides     machine learning     agricultural practices     environmental impact    

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Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

《环境科学与工程前沿（英文）》 2023年 第17卷 第11期 doi: 10.1007/s11783-023-1738-5

摘要：

● A novel integrated machine learning method to analyze O3 changes is proposed.

关键词： Ozone     Integrated method     Machine learning    

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Evaluation and prediction of slope stability using machine learning approaches

《结构与土木工程前沿（英文）》 2021年 第15卷 第4期   页码 821-833 doi: 10.1007/s11709-021-0742-8

摘要： In this paper, the machine learning (ML) model is built for slope stability evaluation and meets the high precision and rapidity requirements in slope engineering. Different ML methods for the factor of safety (FOS) prediction are studied and compared hoping to make the best use of the large variety of existing statistical and ML regression methods collected. The data set of this study includes six characteristics, namely unit weight, cohesion, internal friction angle, slope angle, slope height, and pore water pressure ratio. The whole ML model is primarily divided into data preprocessing, outlier processing, and model evaluation. In the data preprocessing, the duplicated data are first removed, then the outliers are filtered by the LocalOutlierFactor method and finally, the data are standardized. 11 ML methods are evaluated for their ability to learn the FOS based on different input parameter combinations. By analyzing the evaluation indicators R 2, MAE, and MSE of these methods, SVM, GBR, and Bagging are considered to be the best regression methods. The performance and reliability of the nonlinear regression method are slightly better than that of the linear regression method. Also, the SVM-poly method is used to analyze the susceptibility of slope parameters.

关键词： slope stability     factor of safety     regression     machine learning     repeated cross-validation    

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Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

《化学科学与工程前沿（英文）》 2022年 第16卷 第2期   页码 183-197 doi: 10.1007/s11705-021-2073-7

摘要： Flowsheet simulations of chemical processes on an industrial scale require the solution of large systems of nonlinear equations, so that solvability becomes a practical issue. Additional constraints from technical, economic, environmental, and safety considerations may further limit the feasible solution space beyond the convergence requirement. A priori, the design variable domains for which a simulation converges and fulfills the imposed constraints are usually unknown and it can become very time-consuming to distinguish feasible from infeasible design variable choices by simply running the simulation for each choice. To support the exploration of the design variable space for such scenarios, an adaptive sampling technique based on machine learning models has recently been proposed. However, that approach only considers the exploration of the convergent domain and ignores additional constraints. In this paper, we present an improvement which particularly takes the fulfillment of constraints into account. We successfully apply the proposed algorithm to a toy example in up to 20 dimensions and to an industrially relevant flowsheet simulation.

关键词： machine learning     flowsheet simulations     constraints     exploration    

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Predicting torsional capacity of reinforced concrete members by data-driven machine learning models

《结构与土木工程前沿（英文）》 2024年 第18卷 第3期   页码 444-460 doi: 10.1007/s11709-024-1050-x

摘要： Due to the complicated three-dimensional behaviors and testing limitations of reinforced concrete (RC) members in torsion, torsional mechanism exploration and torsional performance prediction have always been difficult. In the present paper, several machine learning models were applied to predict the torsional capacity of RC members. Experimental results of a total of 287 torsional specimens were collected through an overall literature review. Algorithms of extreme gradient boosting machine (XGBM), random forest regression, back propagation artificial neural network and support vector machine, were trained and tested by 10-fold cross-validation method. Predictive performances of proposed machine learning models were evaluated and compared, both with each other and with the calculated results of existing design codes, i.e., GB 50010, ACI 318-19, and Eurocode 2. The results demonstrated that better predictive performance was achieved by machine learning models, whereas GB 50010 slightly overestimated the torsional capacity, and ACI 318-19 and Eurocode 2 underestimated it, especially in the case of ACI 318-19. The XGBM model gave the most favorable predictions with R2 = 0.999, RMSE = 1.386, MAE = 0.86, and λ¯ = 0.976. Moreover, strength of concrete was the most sensitive input parameters affecting the reliability of the predictive model, followed by transverse-to-longitudinal reinforcement ratio and total reinforcement ratio.

关键词： RC members     torsional capacity     machine learning models     design codes    

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Improving lipid production by for renewable fuel production based on machine learning

《化学科学与工程前沿（英文）》 2024年 第18卷 第5期 doi: 10.1007/s11705-024-2410-8

摘要： Microbial lipid fermentation encompasses intricate complex cell growth processes and heavily relies on expert experience for optimal production. Digital modeling of the fermentation process assists researchers in making intelligent decisions, employing logical reasoning and strategic planning to optimize lipid fermentation. It this study, the effects of medium components and concentrations on lipid fermentation were investigated, first. And then, leveraging the collated data, a variety of machine learning algorithms were used to model and optimize the lipid fermentation process. The models, based on artificial neural networks and support vector machines, achieved R2 values all higher than 0.93, ensuring accurate predictions of the fermentation process. Multiple linear regression was used to evaluate the respective target parameter, which were affected by the medium components of lipid fermentation. Lastly, single and multi-objective optimization were conducted for lipid fermentation using the genetic algorithm. Experimental results demonstrated the maximum biomass of 50.3 g·L−1 and maximum lipid concentration of 14.1 g·L−1 with the error between the experimental and predicted values less than 5%. The results of the multi-objective optimization reveal the synergistic and competitive relationship between biomass, lipid concentration, and conversion rate, which lay a basis for in-depth optimization and amplification.

关键词： microbial lipid     machine learning     artificial neural network     support vector machine     genetic algorithm    

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Big data and machine learning: A roadmap towards smart plants

《工程管理前沿（英文）》 2022年 第9卷 第4期   页码 623-639 doi: 10.1007/s42524-022-0218-0

摘要： Industry 4.0 aims to transform chemical and biochemical processes into intelligent systems via the integration of digital components with the actual physical units involved. This process can be thought of as addition of a central nervous system with a sensing and control monitoring of components and regulating the performance of the individual physical assets (processes, units, etc.) involved. Established technologies central to the digital integrating components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced data processing, storage and analysis, advanced process control, artificial intelligence and machine learning, cloud computing, and virtual and augmented reality. An essential element to this transformation is the exploitation of large amounts of historical process data and large volumes of data generated in real-time by smart sensors widely used in industry. Exploitation of the information contained in these data requires the use of advanced machine learning and artificial intelligence technologies integrated with more traditional modelling techniques. The purpose of this paper is twofold: a) to present the state-of-the-art of the aforementioned technologies, and b) to present a strategic plan for their integration toward the goal of an autonomous smart plant capable of self-adaption and self-regulation for short- and long-term production management.

关键词： big data     machine learning     artificial intelligence     smart sensor     cyber–physical system     Industry 4.0     intelligent system     digitalization    

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State-of-the-art applications of machine learning in the life cycle of solid waste management

《环境科学与工程前沿（英文）》 2023年 第17卷 第4期 doi: 10.1007/s11783-023-1644-x

摘要：

● State-of-the-art applications of machine learning (ML) in solid waste (SW) is presented.

关键词： Machine learning (ML)     Solid waste (SW)     Bibliometrics     SW management     Energy utilization     Life cycle    

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Automated identification of steel weld defects, a convolutional neural network improved machine learning

《结构与土木工程前沿（英文）》 2024年 第18卷 第2期   页码 294-308 doi: 10.1007/s11709-024-1045-7

摘要： This paper proposes a machine-learning-based methodology to automatically classify different types of steel weld defects, including lack of the fusion, porosity, slag inclusion, and the qualified (no defects) cases. This methodology solves the shortcomings of existing detection methods, such as expensive equipment, complicated operation and inability to detect internal defects. The study first collected percussed data from welded steel members with or without weld defects. Then, three methods, the Mel frequency cepstral coefficients, short-time Fourier transform (STFT), and continuous wavelet transform were implemented and compared to explore the most appropriate features for classification of weld statuses. Classic and convolutional neural network-enhanced algorithms were used to classify, the extracted features. Furthermore, experiments were designed and performed to validate the proposed method. Results showed that STFT achieved higher accuracies (up to 96.63% on average) in the weld status classification. The convolutional neural network-enhanced support vector machine (SVM) outperformed six other algorithms with an average accuracy of 95.8%. In addition, random forest and SVM were efficient approaches with a balanced trade-off between the accuracies and the computational efforts.

关键词： steel weld     machine learning     convolutional neural network     weld defect detection     classification task     percussion    

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Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual

《环境科学与工程前沿（英文）》 2024年 第18卷 第2期 doi: 10.1007/s11783-024-1777-6

摘要：

● A machine learning approach was applied to predict free chlorine residuals.

关键词： Machine learning     Data-driven modeling     Drinking water treatment     Disinfection     Chlorination    

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Development of machine learning multi-city model for municipal solid waste generation prediction

《环境科学与工程前沿（英文）》 2022年 第16卷 第9期 doi: 10.1007/s11783-022-1551-6

摘要：

● A database of municipal solid waste (MSW) generation in China was established.

关键词： Municipal solid waste     Machine learning     Multi-cities     Gradient boost regression tree    

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Machine learning in building energy management: A critical review and future directions

《工程管理前沿（英文）》 2022年 第9卷 第2期   页码 239-256 doi: 10.1007/s42524-021-0181-1

摘要： Over the past two decades, machine learning (ML) has elicited increasing attention in building energy management (BEM) research. However, the boundary of the ML-BEM research has not been clearly defined, and no thorough review of ML applications in BEM during the whole building life-cycle has been published. This study aims to address this gap by reviewing the ML-BEM papers to ascertain the status of this research area and identify future research directions. An integrated framework of ML-BEM, composed of four layers and a series of driving factors, is proposed. Then, based on the hype cycle model, this paper analyzes the current development status of ML-BEM and tries to predict its future development trend. Finally, five research directions are discussed: (1) the behavioral impact on BEM, (2) the integration management of renewable energy, (3) security concerns of ML-BEM, (4) extension to other building life-cycle phases, and (5) the focus on fault detection and diagnosis. The findings of this study are believed to provide useful references for future research on ML-BEM.

关键词： building energy management     machine learning     integrated framework     knowledge evolution    

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Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

《医学前沿（英文）》 2023年 第17卷 第4期   页码 768-780 doi: 10.1007/s11684-023-0982-1

摘要： Previous studies have revealed that patients with hypertrophic cardiomyopathy (HCM) exhibit differences in symptom severity and prognosis, indicating potential HCM subtypes among these patients. Here, 793 patients with HCM were recruited at an average follow-up of 32.78 ± 27.58 months to identify potential HCM subtypes by performing consensus clustering on the basis of their echocardiography features. Furthermore, we proposed a systematic method for illustrating the relationship between the phenotype and genotype of each HCM subtype by using machine learning modeling and interactome network detection techniques based on whole-exome sequencing data. Another independent cohort that consisted of 414 patients with HCM was recruited to replicate the findings. Consequently, two subtypes characterized by different clinical outcomes were identified in HCM. Patients with subtype 2 presented asymmetric septal hypertrophy associated with a stable course, while those with subtype 1 displayed left ventricular systolic dysfunction and aggressive progression. Machine learning modeling based on personal whole-exome data identified 46 genes with mutation burden that could accurately predict subtype propensities. Furthermore, the patients in another cohort predicted as subtype 1 by the 46-gene model presented increased left ventricular end-diastolic diameter and reduced left ventricular ejection fraction. By employing echocardiography and genetic screening for the 46 genes, HCM can be classified into two subtypes with distinct clinical outcomes.

关键词： machine learning methods     hypertrophic cardiomyopathy     genetic risk    

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Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies

《环境科学与工程前沿（英文）》 2023年 第17卷 第12期 doi: 10.1007/s11783-023-1752-7

摘要：

● Online learning models accurately predict influent flow rate at wastewater plants.

关键词： Wastewater prediction     Data stream     Online learning     Batch learning     Influent flow rates    

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标题 作者 时间 类型 操作

Spatial prediction of soil contamination based on machine learning: a review

期刊论文

Advancing agriculture with machine learning: a new frontier in weed management

期刊论文

Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

期刊论文

Evaluation and prediction of slope stability using machine learning approaches

期刊论文

Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

期刊论文

Predicting torsional capacity of reinforced concrete members by data-driven machine learning models

期刊论文

Improving lipid production by for renewable fuel production based on machine learning

期刊论文

Big data and machine learning: A roadmap towards smart plants

期刊论文

State-of-the-art applications of machine learning in the life cycle of solid waste management

期刊论文

Automated identification of steel weld defects, a convolutional neural network improved machine learning

期刊论文

Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual

期刊论文

Development of machine learning multi-city model for municipal solid waste generation prediction

期刊论文

Machine learning in building energy management: A critical review and future directions

期刊论文

Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

期刊论文

Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies

期刊论文