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Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel

Zaobao LIU; Yongchen WANG; Long LI; Xingli FANG; Junze WANG

《结构与土木工程前沿（英文）》 2022年 第16卷 第4期   页码 401-413 doi: 10.1007/s11709-022-0823-3

摘要： Real-time dynamic adjustment of the tunnel bore machine (TBM) advance rate according to the rock-machine interaction parameters is of great significance to the adaptability of TBM and its efficiency in construction. This paper proposes a real-time predictive model of TBM advance rate using the temporal convolutional network (TCN), based on TBM construction big data. The prediction model was built using an experimental database, containing 235 data sets, established from the construction data from the Jilin Water-Diversion Tunnel Project in China. The TBM operating parameters, including total thrust, cutterhead rotation, cutterhead torque and penetration rate, are selected as the input parameters of the model. The TCN model is found outperforming the recurrent neural network (RNN) and long short-term memory (LSTM) model in predicting the TBM advance rate with much smaller values of mean absolute percentage error than the latter two. The penetration rate and cutterhead torque of the current moment have significant influence on the TBM advance rate of the next moment. On the contrary, the influence of the cutterhead rotation and total thrust is moderate. The work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel construction.

关键词： hard rock tunnel     tunnel bore machine advance rate prediction     temporal convolutional networks     soft computing     construction big data    

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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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Effect of cutterhead configuration on tunnel face stability during shield machine maintenance outages

《结构与土木工程前沿（英文）》 2023年 第17卷 第4期   页码 522-532 doi: 10.1007/s11709-023-0930-9

摘要： Owing to long-distance advancement or obstacles, shield tunneling machines are typically shut down for maintenance. Engineering safety during maintenance outages is determined by the stability of the tunnel face. Pressure maintenance openings are typically used under complicated hydrogeological conditions. The tunnel face is supported by a medium at the bottom of the excavation chamber and compressed air at the top. Owing to the high risk of face failure, the necessity of support pressure when cutterhead support is implemented and a method for determining the value of compressed air pressure using different support ratios must to be determined. In this study, a non-fully chamber supported rotational failure model considering cutterhead support is developed based on the upper-bound theorem of limit analysis. Numerical simulation is conducted to verify the accuracy of the proposed model. The results indicate that appropriately increasing the specific gravity of the supporting medium can reduce the risk of collapse. The required compressed air pressure increases significantly as the support ratio decreases. Disregarding the supporting effect of the cutterhead will result in a tunnel face with underestimated stability. To satisfy the requirement of chamber openings at atmospheric pressure, the stratum reinforcement strength and range at the shield end are provided based on different cutterhead aperture ratios.

关键词： tunnel face stability     cutterhead configuration     aperture ratio     pressure gradient     support ratio    

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Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

《结构与土木工程前沿（英文）》 2013年 第7卷 第1期   页码 72-82 doi: 10.1007/s11709-013-0185-y

摘要： A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibility as a classification problem, which is an imperative task in earthquake engineering. This paper examines the potential of SVM model in prediction of liquefaction using actual field cone penetration test (CPT) data from the 1999 Chi-Chi, Taiwan earthquake. The SVM, a novel learning machine based on statistical theory, uses structural risk minimization (SRM) induction principle to minimize the error. Using cone resistance ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefaction using SVM. Further an attempt has been made to simplify the model, requiring only two parameters ( and maximum horizontal acceleration ), for prediction of liquefaction. Further, developed SVM model has been applied to different case histories available globally and the results obtained confirm the capability of SVM model. For Chi-Chi earthquake, the model predicts with accuracy of 100%, and in the case of global data, SVM model predicts with accuracy of 89%. The effect of capacity factor ( ) on number of support vector and model accuracy has also been investigated. The study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based on field CPT data.

关键词： earthquake     cone penetration test     liquefaction     support vector machine (SVM)     prediction    

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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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Clogging of slurry-shield tunnel-boring machine drives in sedimentary soft rock: A case study

《结构与土木工程前沿（英文）》 doi: 10.1007/s11709-023-0984-8

摘要： This paper presents a case study of the clogging of a slurry-shield tunnel-boring machine (TBM) experienced during tunnel operations in clay-rich argillaceous siltstones under the Ganjiang River, China. The clogging experienced during tunneling was due to special geological conditions, which had a considerably negative impact on the slurry-shield TBM tunneling performance. In this case study, the effect of clogging on the slurry-shield TBM tunneling performance (e.g., advance speed, thrust, torque, and penetration per revolution) was fully investigated. The potential for clogging during tunnel operations in argillaceous siltstone was estimated using an existing empirical classification chart. Many improvement measures have been proposed to mitigate the clogging potential of two slurry-shield TBMs during tunneling, such as the use of an optimum cutting wheel, a replacement cutting tool, improvements to the circulation flushing system and slurry properties, mixed support integrating slurry, and compressed air to support the excavation face. The mechanisms and potential causes of clogging are explained in detail, and the contributions of these mitigation measures to tunneling performance are discussed. By investigating the actual operational parameters of the slurry-shield TBMs, these mitigation measures were proven to be effective in mitigating the clogging potential of slurry-shield TBMs. This case study provides valuable information for slurry-shield TBMs involving tunneling in clay-rich sedimentary rocks.

关键词： slurry-shield TBM     geological investigation     clogging     argillaceous siltstone     TBM performance     mitigation measures    

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Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

《化学科学与工程前沿（英文）》 2022年 第16卷 第4期   页码 523-535 doi: 10.1007/s11705-021-2083-5

摘要： Solubility has been widely regarded as a fundamental property of small molecule drugs and drug candidates, as it has a profound impact on the crystallization process. Solubility prediction, as an alternative to experiments which can reduce waste and improve crystallization process efficiency, has attracted increasing attention. However, there are still many urgent challenges thus far. Herein we used seven descriptors based on understanding dissolution behavior to establish two solubility prediction models by machine learning algorithms. The solubility data of 120 active pharmaceutical ingredients (APIs) in ethanol were considered in the prediction models, which were constructed by random decision forests and artificial neural network with optimized data structure and model accuracy. Furthermore, a comparison with traditional prediction methods including the modified solubility equation and the quantitative structure-property relationships model was carried out. The highest accuracy shown by the testing set proves that the ML models have the best solubility prediction ability. Multiple linear regression and stepwise regression were used to further investigate the critical factor in determining solubility value. The results revealed that the API properties and the solute-solvent interaction both provide a nonnegligible contribution to the solubility value.

关键词： solubility prediction     machine learning     artificial neural network     random decision forests    

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Short-term prediction of influent flow rate and ammonia concentration in municipal wastewater treatment

Shuai MA, Siyu ZENG, Xin DONG, Jining CHEN, Gustaf OLSSON

《环境科学与工程前沿（英文）》 2014年 第8卷 第1期   页码 128-136 doi: 10.1007/s11783-013-0598-9

摘要： The prediction of the influent load is of great importance for the improvement of the control system to a large wastewater treatment plant. A systematic data analysis method is presented in this paper in order to estimate and predict the periodicity of the influent flow rate and ammonia (NH ) concentrations: 1) data filtering using wavelet decomposition and reconstruction; 2) typical cycle identification using power spectrum density analysis; 3) fitting and prediction model establishment based on an autoregressive model. To give meaningful information for feedforward control systems, predictions in different time scales are tested to compare the corresponding predicting accuracy. Considering the influence of the rainfalls, a linear fitting model is derived to estimate the relationship between flow rate trend and rain events. Measurements used to support coefficient fitting and model testing are acquired from two municipal wastewater treatment plants in China. The results show that 1) for both of the two plants, the periodicity affects the flow rate and NH concentrations in different cycles (especially cycles longer than 1 day); 2) when the flow rate and NH concentrations present an obvious periodicity, the decreasing of prediction accuracy is not distinct with increasing of the prediction time scales; 3) the periodicity influence is larger than rainfalls; 4) the rainfalls will make the periodicity of flow rate less obvious in intensive rainy periods.

关键词： influent load prediction     wavelet de-noising     power spectrum density     autoregressive model     time-frequency analysis     wastewater treatment    

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Prediction of shield tunneling-induced ground settlement using machine learning techniques

Renpeng CHEN, Pin ZHANG, Huaina WU, Zhiteng WANG, Zhiquan ZHONG

《结构与土木工程前沿（英文）》 2019年 第13卷 第6期   页码 1363-1378 doi: 10.1007/s11709-019-0561-3

摘要： Predicting the tunneling-induced maximum ground surface settlement is a complex problem since the settlement depends on plenty of intrinsic and extrinsic factors. This study investigates the efficiency and feasibility of six machine learning (ML) algorithms, namely, back-propagation neural network, wavelet neural network, general regression neural network (GRNN), extreme learning machine, support vector machine and random forest (RF), to predict tunneling-induced settlement. Field data sets including geological conditions, shield operational parameters, and tunnel geometry collected from four sections of tunnel with a total of 3.93 km are used to build models. Three indicators, mean absolute error, root mean absolute error, and coefficient of determination the ( ) are used to demonstrate the performance of each computational model. The results indicated that ML algorithms have great potential to predict tunneling-induced settlement, compared with the traditional multivariate linear regression method. GRNN and RF algorithms show the best performance among six ML algorithms, which accurately recognize the evolution of tunneling-induced settlement. The correlation between the input variables and settlement is also investigated by Pearson correlation coefficient.

关键词： EPB shield     shield tunneling     settlement prediction     machine learning    

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Construction risks of Huaying mount tunnel and countermeasures

Haibo YAO, Feng GAO, Shigang YU, Wei DANG

《结构与土木工程前沿（英文）》 2017年 第11卷 第3期   页码 279-285 doi: 10.1007/s11709-017-0414-x

摘要： The Chongqing-Guang’an motorway is planned to cross Huaying mount at Jingguan town of Chongqing city. The whole mount is a colossal anticline whose core is consisted of coal measure strata (upper Permian Longtan formation P l) and the limbs are limestone strata (middle Triassic Leikoupo formation T l and lower Triassic Jialingjiang formation T j). The tunneling is full of risks of collapse, gas explosion or gas outburst, water (mud) inrush, gas inrush because of existence of faults, high pressure gas, karst tectonics and coal goafs around the tunnel. In order to cope with the high risk, two main countermeasures were taken to ensure security of construction. One is geology prediction, and the other is automatic wireless real-time monitoring system, which contains monitoring of video, wind speed, poisonous gas (CH , CO, H S, SO ), people location, and automatic power-off equipment while gas contents being more than warning threshold. These ascertained the engineering safety effectively.

关键词： tunnel?construction     gas?outburst     geology?prediction     automatic?monitoring?system    

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Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated

《环境科学与工程前沿（英文）》 2023年 第17卷 第11期 doi: 10.1007/s11783-023-1735-8

摘要：

● Data-driven approach was used to simulate VFA production from WAS fermentation.

关键词： Machine learning     Volatile fatty acids     Riboflavin     Waste activated sludge     eXtreme Gradient Boosting    

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Shear stress distribution prediction in symmetric compound channels using data mining and machine learning

Zohreh SHEIKH KHOZANI, Khabat KHOSRAVI, Mohammadamin TORABI, Amir MOSAVI, Bahram REZAEI, Timon RABCZUK

《结构与土木工程前沿（英文）》 2020年 第14卷 第5期   页码 1097-1109 doi: 10.1007/s11709-020-0634-3

摘要： Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning algorithms including Random Forest (RF), M5P, Random Committee, KStar and Additive Regression implemented on attained data to predict the shear stress distribution in the compound channel. Results indicated among these five models; RF method indicated the most precise results with the highest value of 0.9. Finally, the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.

关键词： compound channel     machine learning     SKM model     shear stress distribution     data mining models    

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An energy consumption prediction approach of die casting machines driven by product parameters

《机械工程前沿（英文）》 2021年 第16卷 第4期   页码 868-886 doi: 10.1007/s11465-021-0656-0

摘要： Die casting machines, which are the core equipment of the machinery manufacturing industry, consume great amounts of energy. The energy consumption prediction of die casting machines can support energy consumption quota, process parameter energy-saving optimization, energy-saving design, and energy efficiency evaluation; thus, it is of great significance for Industry 4.0 and green manufacturing. Nevertheless, due to the uncertainty and complexity of the energy consumption in die casting machines, there is still a lack of an approach for energy consumption prediction that can provide support for process parameter optimization and product design taking energy efficiency into consideration. To fill this gap, this paper proposes an energy consumption prediction approach for die casting machines driven by product parameters. Firstly, the system boundary of energy consumption prediction is defined, and subsequently, based on the energy consumption characteristics analysis, a theoretical energy consumption model is established. Consequently, a systematic energy consumption prediction approach for die casting machines, involving product, die, equipment, and process parameters, is proposed. Finally, the feasibility and reliability of the proposed energy consumption prediction approach are verified with the help of three die casting machines and six types of products. The results show that the prediction accuracy of production time and energy consumption reached 91.64% and 85.55%, respectively. Overall, the proposed approach can be used for the energy consumption prediction of different die casting machines with different products.

关键词： die casting machine     energy consumption prediction     product parameters    

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Understanding the demand predictability of bike share systems: A station-level analysis

《工程管理前沿（英文）》   页码 551-565 doi: 10.1007/s42524-023-0279-8

摘要： Predicting demand for bike share systems (BSSs) is critical for both the management of an existing BSS and the planning for a new BSS. While researchers have mainly focused on improving prediction accuracy and analysing demand-influencing factors, there are few studies examining the inherent randomness of stations’ observed demands and to what degree the demands at individual stations are predictable. Using Divvy bike-share one-year data from Chicago, USA, we measured demand entropy and quantified the station-level predictability. Additionally, to verify that these predictability measures could represent the performance of prediction models, we implemented two commonly used demand prediction models to compare the empirical prediction accuracy with the calculated entropy and predictability. Furthermore, we explored how city- and system-specific temporally-constant features would impact entropy and predictability to inform estimating these measures when historical demand data are unavailable. Our results show that entropy of demands across stations is polarized as some stations exhibit high uncertainty (a low entropy of 0.65) and others have almost no check-out demand uncertainty (a high entropy of around 1.0). We also validated that the entropy and predictability are a priori model-free indicators for prediction error, given a sequence of bike usage demands. Lastly, we identified that key factors contributing to station-level entropy and predictability include per capita income, spatial eccentricity, and the number of parking lots near the station. Findings from this study provide more fundamental understanding of BSS demand prediction, which can help decision makers and system operators anticipate diverse station-level prediction errors from their prediction models both for existing stations and for new ones.

关键词： bike share systems     demand prediction     prediction errors     machine learning     entropy    

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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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标题 作者 时间 类型 操作

Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel

Zaobao LIU; Yongchen WANG; Long LI; Xingli FANG; Junze WANG

期刊论文

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

期刊论文

Effect of cutterhead configuration on tunnel face stability during shield machine maintenance outages

期刊论文

Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

期刊论文

Evaluation and prediction of slope stability using machine learning approaches

期刊论文

Clogging of slurry-shield tunnel-boring machine drives in sedimentary soft rock: A case study

期刊论文

Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

期刊论文

Short-term prediction of influent flow rate and ammonia concentration in municipal wastewater treatment

Shuai MA, Siyu ZENG, Xin DONG, Jining CHEN, Gustaf OLSSON

期刊论文

Prediction of shield tunneling-induced ground settlement using machine learning techniques

Renpeng CHEN, Pin ZHANG, Huaina WU, Zhiteng WANG, Zhiquan ZHONG

期刊论文

Construction risks of Huaying mount tunnel and countermeasures

Haibo YAO, Feng GAO, Shigang YU, Wei DANG

期刊论文

Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated

期刊论文

Shear stress distribution prediction in symmetric compound channels using data mining and machine learning

Zohreh SHEIKH KHOZANI, Khabat KHOSRAVI, Mohammadamin TORABI, Amir MOSAVI, Bahram REZAEI, Timon RABCZUK

期刊论文

An energy consumption prediction approach of die casting machines driven by product parameters

期刊论文

Understanding the demand predictability of bike share systems: A station-level analysis

期刊论文

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

期刊论文