Soil desiccation cracking is ubiquitous in nature and has significantpotential impacts on the engineering geological properties of soils.Previous studies have extensively examined various factors affecting soil cracki...Soil desiccation cracking is ubiquitous in nature and has significantpotential impacts on the engineering geological properties of soils.Previous studies have extensively examined various factors affecting soil cracking behavior through a numerous small-sample experiments.However,experimental studies alone cannot accurately describe soil cracking behavior.In this study,we firstly propose a modeling framework for predicting the surface crack ratio of soil desiccation cracking based on machine learning and interpretable analysis.The framework utilizes 1040 sets of soil cracking experimental data and employs random forest(RF),extreme gradient boosting(XGBoost),and artificialneural network(ANN)models to predict the surface crack ratio of soil desiccation cracking.To clarify the influenceof input features on soil cracking behavior,feature importance and Shapley additive explanations(SHAP)are applied for interpretability analysis.The results reveal that ensemble methods(RF and XGBoost)provide better predictive performance than the deep learning model(ANN).The feature importance analysis shows that soil desiccation cracking is primarily influencedby initial water content,plasticity index,finalwater content,liquid limit,sand content,clay content and thickness.Moreover,SHAP-based interpretability analysis further explores how soil cracking responds to various input variables.This study provides new insight into the evolution of soil cracking behavior,enhancing the understanding of its physical mechanisms and facilitating the assessment of potential regional development of soil desiccation cracking.展开更多
By dint of V-3θ diagram from the Blown-up theory,a continuous heavy rain process in western Sichuan basin from July 14 to 17,2009 is analyzed in this paper.Situation field and precipitation of ECWMF and T213 are veri...By dint of V-3θ diagram from the Blown-up theory,a continuous heavy rain process in western Sichuan basin from July 14 to 17,2009 is analyzed in this paper.Situation field and precipitation of ECWMF and T213 are verified and discussed.Results show that V-3θ diagram can describe the heavy rain process accurately.Combining with additional conventional weather charts,experience and numerical forecast products,the heavy rain falling area is determined.The forecast accuracy of situation field of EC is significantly higher than that of T213 and the forecast accuracy of T213 for heavy rain forecast is relatively low.展开更多
In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken a...In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.展开更多
The quality problem of the concrete body and backwall grouting of shaft lining must be taken into consideration during the engineering construction of the shaft. Detection and evaluation are needed to determine the pa...The quality problem of the concrete body and backwall grouting of shaft lining must be taken into consideration during the engineering construction of the shaft. Detection and evaluation are needed to determine the parameters such as the location and depth of drilling. The record of elastic wave can be gained through laying the surveying lines of the ring and ver- tical direction in the shaft lining by the elastic wave method. And specifically, through analyzing the different parameters of seismic attribute such as the velocity of high frequency reflection wave, amplitude and frequency, the abnormal range on the wall or under the wall can be forecasted. The concrete quality of shallow layer in the shaft lining can be evaluated through the velocity of surfer wave. Using the evaluating technique of comprehensive frequency and the phase feature of waveform, the basic features such as inner construction, wall back filling and failure depth of shaft lining can be interpreted from qualitatively to half quantitatively, and the interpreting section can be drawn. The results show that the detection effect for the shaft quality is significant by elastic wave technique, and the delineation of abnormal areas is accurate. Its guidance function is better for pro- duction.展开更多
Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical str...Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix, which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISM to analyze the main factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.展开更多
Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalizati...Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalization method for multivariate correlated alarms to realize the root cause analysis and alarm prioritization. An information fusion based interpretive structural model is constructed according to the data-driven partial correlation coefficient calculation and process knowledge modification. This hierarchical multi-layer model is helpful in abnormality propagation path identification and root-cause analysis. Revised Likert scale method is adopted to determine the alarm priority and reduce the blindness of alarm handling. As a case study, the Tennessee Eastman process is utilized to show the effectiveness and validity of proposed approach. Alarm system performance comparison shows that our rationalization methodology can reduce the alarm flood to some extent and improve the performance.展开更多
Understanding protein corona composition is essential for evaluating their potential applications in biomedicine.Relative protein abundance(RPA),accounting for the total proteins in the corona,is an important paramete...Understanding protein corona composition is essential for evaluating their potential applications in biomedicine.Relative protein abundance(RPA),accounting for the total proteins in the corona,is an important parameter for describing the protein corona.For the first time,we comprehensively predicted the RPA of multiple proteins on the protein corona.First,we used multiple machine learning algorithms to predict whether a protein adsorbs to a nanoparticle,which is dichotomous prediction.Then,we selected the top 3 performing machine learning algorithms in dichotomous prediction to predict the specific value of RPA,which is regression prediction.Meanwhile,we analyzed the advantages and disadvantages of different machine learning algorithms for RPA prediction through interpretable analysis.Finally,we mined important features about the RPA prediction,which provided effective suggestions for the preliminary design of protein corona.The service for the prediction of RPA is available at http://www.bioai-lab.com/PC_ML.展开更多
Coaxing out desired behavior from pretrained models,while avoiding undesirable ones,has redefined Natural Language Processing(NLP)and is reshaping how we interact with computers.What was once a scientific engineering ...Coaxing out desired behavior from pretrained models,while avoiding undesirable ones,has redefined Natural Language Processing(NLP)and is reshaping how we interact with computers.What was once a scientific engineering discipline-in which building blocks are stacked one on top of the other-is arguably already a complex systems science-in which emergent behaviors are sought out to support previously unimagined use cases.Despite the ever increasing number of benchmarks that measure task performance,we lack explanations of what behaviors language models exhibit that allow them to complete these tasks in the first place.We argue for a systematic effort to decompose language model behavior into categories that explain cross-task performance,to guide mechanistic explanations and help future-proof analytic research.展开更多
基金supported by the National Key Research and Development Program of China(Grant Nos.2023YFC3707900 and 2024YFC3012700)the National Natural Science Foundation of China(Grant No.42230710).
文摘Soil desiccation cracking is ubiquitous in nature and has significantpotential impacts on the engineering geological properties of soils.Previous studies have extensively examined various factors affecting soil cracking behavior through a numerous small-sample experiments.However,experimental studies alone cannot accurately describe soil cracking behavior.In this study,we firstly propose a modeling framework for predicting the surface crack ratio of soil desiccation cracking based on machine learning and interpretable analysis.The framework utilizes 1040 sets of soil cracking experimental data and employs random forest(RF),extreme gradient boosting(XGBoost),and artificialneural network(ANN)models to predict the surface crack ratio of soil desiccation cracking.To clarify the influenceof input features on soil cracking behavior,feature importance and Shapley additive explanations(SHAP)are applied for interpretability analysis.The results reveal that ensemble methods(RF and XGBoost)provide better predictive performance than the deep learning model(ANN).The feature importance analysis shows that soil desiccation cracking is primarily influencedby initial water content,plasticity index,finalwater content,liquid limit,sand content,clay content and thickness.Moreover,SHAP-based interpretability analysis further explores how soil cracking responds to various input variables.This study provides new insight into the evolution of soil cracking behavior,enhancing the understanding of its physical mechanisms and facilitating the assessment of potential regional development of soil desiccation cracking.
基金Supported by Civil Aviation Flight University of China Natural Science Fund Program(J2008-66)~~
文摘By dint of V-3θ diagram from the Blown-up theory,a continuous heavy rain process in western Sichuan basin from July 14 to 17,2009 is analyzed in this paper.Situation field and precipitation of ECWMF and T213 are verified and discussed.Results show that V-3θ diagram can describe the heavy rain process accurately.Combining with additional conventional weather charts,experience and numerical forecast products,the heavy rain falling area is determined.The forecast accuracy of situation field of EC is significantly higher than that of T213 and the forecast accuracy of T213 for heavy rain forecast is relatively low.
基金This work is funded by the National Natural Science Foundation of China(Grant Nos.42377164 and 52079062)the National Science Fund for Distinguished Young Scholars of China(Grant No.52222905).
文摘In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.
文摘The quality problem of the concrete body and backwall grouting of shaft lining must be taken into consideration during the engineering construction of the shaft. Detection and evaluation are needed to determine the parameters such as the location and depth of drilling. The record of elastic wave can be gained through laying the surveying lines of the ring and ver- tical direction in the shaft lining by the elastic wave method. And specifically, through analyzing the different parameters of seismic attribute such as the velocity of high frequency reflection wave, amplitude and frequency, the abnormal range on the wall or under the wall can be forecasted. The concrete quality of shallow layer in the shaft lining can be evaluated through the velocity of surfer wave. Using the evaluating technique of comprehensive frequency and the phase feature of waveform, the basic features such as inner construction, wall back filling and failure depth of shaft lining can be interpreted from qualitatively to half quantitatively, and the interpreting section can be drawn. The results show that the detection effect for the shaft quality is significant by elastic wave technique, and the delineation of abnormal areas is accurate. Its guidance function is better for pro- duction.
基金Supported by the National Natural Science Foundation of China(61374166,6153303)the Doctoral Fund of Ministry of Education of China(20120010110010)the Fundamental Research Funds for the Central Universities(YS1404,JD1413,ZY1502)
文摘Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix, which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISM to analyze the main factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.
基金Supported by the National Natural Science Foundation of China(61473026,61104131)the Fundamental Research Funds for the Central Universities(JD1413)
文摘Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalization method for multivariate correlated alarms to realize the root cause analysis and alarm prioritization. An information fusion based interpretive structural model is constructed according to the data-driven partial correlation coefficient calculation and process knowledge modification. This hierarchical multi-layer model is helpful in abnormality propagation path identification and root-cause analysis. Revised Likert scale method is adopted to determine the alarm priority and reduce the blindness of alarm handling. As a case study, the Tennessee Eastman process is utilized to show the effectiveness and validity of proposed approach. Alarm system performance comparison shows that our rationalization methodology can reduce the alarm flood to some extent and improve the performance.
基金supported by the National Natural Science Foundation of China(nos.62i01100 and 62262015).
文摘Understanding protein corona composition is essential for evaluating their potential applications in biomedicine.Relative protein abundance(RPA),accounting for the total proteins in the corona,is an important parameter for describing the protein corona.For the first time,we comprehensively predicted the RPA of multiple proteins on the protein corona.First,we used multiple machine learning algorithms to predict whether a protein adsorbs to a nanoparticle,which is dichotomous prediction.Then,we selected the top 3 performing machine learning algorithms in dichotomous prediction to predict the specific value of RPA,which is regression prediction.Meanwhile,we analyzed the advantages and disadvantages of different machine learning algorithms for RPA prediction through interpretable analysis.Finally,we mined important features about the RPA prediction,which provided effective suggestions for the preliminary design of protein corona.The service for the prediction of RPA is available at http://www.bioai-lab.com/PC_ML.
文摘Coaxing out desired behavior from pretrained models,while avoiding undesirable ones,has redefined Natural Language Processing(NLP)and is reshaping how we interact with computers.What was once a scientific engineering discipline-in which building blocks are stacked one on top of the other-is arguably already a complex systems science-in which emergent behaviors are sought out to support previously unimagined use cases.Despite the ever increasing number of benchmarks that measure task performance,we lack explanations of what behaviors language models exhibit that allow them to complete these tasks in the first place.We argue for a systematic effort to decompose language model behavior into categories that explain cross-task performance,to guide mechanistic explanations and help future-proof analytic research.
