Visual tracking has been widely applied in construction industry and attracted signifi-cant interests recently. Lots of research studies have adopted visual tracking techniques on the surveillance of construction work...Visual tracking has been widely applied in construction industry and attracted signifi-cant interests recently. Lots of research studies have adopted visual tracking techniques on the surveillance of construction workforce, project productivity and construction safety. Until now, visual tracking algorithms have gained promising performance when tracking un-articulated equipment in construction sites. However, state-of-art tracking algorithms have unguaranteed performance in tracking articulated equipment, such as backhoes and excavators. The stretching buckets and booms are the main obstacles of successfully tracking articulated equipment. In order to fill this knowledge gap, the part-based tracking algorithms are introduced in this paper for tracking articulated equipment in construction sites. The part-based tracking is able to track different parts of target equipment while using multiple tracking algorithms at the same sequence. Some existing tracking methods have been chosen according to their outstanding performance in the computer vision community. Then, the part-based algorithms were created on the basis of selected visual tracking methods and tested by real construction sequences. In this way, the tracking performance was evaluated from effectiveness and robustness aspects. Throughout the quantification analysis, the tracking performance of articulated equipment was much more improved by using the part-based tracking algorithms.展开更多
Hydraulic excavator is one type of the most widely applied construction equipment for various applications mainly because of its versatility and mobility.Among the tasks performed by a hydraulic excavator,repeatable l...Hydraulic excavator is one type of the most widely applied construction equipment for various applications mainly because of its versatility and mobility.Among the tasks performed by a hydraulic excavator,repeatable level digging or flat surface finishing may take a large percentage.Using automated functions to perform such repeatable and tedious jobs will not only greatly increase the overall productivity but more importantly also improve the operation safety.For the purpose of investigating the technology without loss of generality,this research is conducted to create a coordinate control method for the boom,arm and bucket cylinders on a hydraulic excavator to perform accurate and effective works.On the basis of the kinematic analysis of the excavator linkage system,the tip trajectory of the end-effector can be determined in terms of three hydraulic cylinders coordinated motion with a visualized method.The coordination of those hydraulic cylinders is realized by controlling three electro-hydraulic proportional valves coordinately.Therefore,the complex control algorithm of a hydraulic excavator can be simplified into coordinated motion control of three individual systems.This coordinate control algorithm was validated on a wheeled hydraulic excavator,and the validation results indicated that this developed control method could satisfactorily accomplish the auto-digging function for level digging or flat surface finishing.展开更多
The determination of digging resistance is of decisive importance when evaluating the mining process of digging-wheel excavators. Methodic of measuring and determinant relations （formulae） are different in various c...The determination of digging resistance is of decisive importance when evaluating the mining process of digging-wheel excavators. Methodic of measuring and determinant relations （formulae） are different in various countries and by the comparison of them, underlying data for complex and objective evaluation using the knowledge from the field of rock disintegration can be obtained. In virtue of analysis of many measurements in situ, rather high specific power consumption with a wheel of excavator has been proved. What was the criterion for evaluation was the power consumption related to the volume of a rock disintegrated per unit time. From theoretical analyses, possibilities of eliminating a negative influence of the human factor upon the mining process and thus possibilities of optimum utilizing the power input of a wheel drive follow.展开更多
Dynamic disturbances with various frequencies could trigger different failure modes of deep excavations.Superimposed on this static stress are dynamic disturbances due to various dynamic vibrations,e.g.excavation blas...Dynamic disturbances with various frequencies could trigger different failure modes of deep excavations.Superimposed on this static stress are dynamic disturbances due to various dynamic vibrations,e.g.excavation blasting,blasting,tunnel boring machine(TBM)vibration,rockburst wave,earthquakes.Specifically,these dynamic sources are characterized by a wide range of wave frequencies f,resulting in differences in failure modes.A series of true-triaxial compression tests were conducted on granite to simulate the excavation-induced stress path in three-dimensional(3D)stresses.Subsequently,a dynamic disturbance with various frequencies was applied to a cuboid specimen,to reveal the behavior associated with brittle failure.The dynamic disturbance with frequencies f of 5 Hz,10 Hz,and 40 Hz generates less disturbed energy components in the granite together with higher peak strength.However,dynamic disturbances with f of 20 Hz and 30 Hz resulted in a lower peak strength;the peak strength of the rock increases sp albeit it decreases at first,then increases.This U-shaped phenomenon relates to the natural frequency of the granite under such stress conditions.Different rock lithologies consisting of diverse mineral composition,respond differently to each sensitive resonance frequency.Interestingly,the weak disturbance stress with a high frequency f and low amplitude A increases the ratio of crack damage to peak strength(scd/sp)in the granite.This leads to the inhibition of the expansion of the granite during the dynamic disturbance process.Multiple penetrating tensileeshear cracks appear in the s3-direction as the disturbance frequency f increases.展开更多
Grouting with water–cement mixtures is the most widely used and cost-effective method for managing excess water inflow during tunnel construction.Due to uncertain geological and hydrological conditions,current grouti...Grouting with water–cement mixtures is the most widely used and cost-effective method for managing excess water inflow during tunnel construction.Due to uncertain geological and hydrological conditions,current grouting design relies heavily on the experience of onsite engineers.Recent advances in machine learning offer a promising alternative to traditional design to predict grout volume and improve grouting efficiency.Here,an artificial neural network(ANN)model was developed using the data set from an operation tunnel of Jurong Rock Caverns in Singapore to showcase an efficient and physics-guided training strategy.The ANN model was refined by incorporating the spatial scenarios,including the number of grouting holes in four quadrants of tunneling faces,the sequence of grouting screens along the tunnel axis,and the order of grouting rounds on the tunneling faces.The results indicate that an improved training strategy should encompass the grouting process,from Round 1 with grouting holes uniformly distributed around the tunnel periphery,to Round 2 with grouting holes drilled midway between neighboring first-round holes,and to Round 3 with grouting holes determined by onsite engineers.This model,trained based on the order of grouting rounds,performs better than the other models,highlighting the importance of establishing machine learning models grounded in physical principles.The finding was verified by the data set from another operation tunnel and concluded with a perspective on future grouting research.展开更多
A distributed fault-tolerant strategy for the controller area network based electric swing system of hybrid excavators is proposed to achieve good performance under communication errors based on the adaptive compensat...A distributed fault-tolerant strategy for the controller area network based electric swing system of hybrid excavators is proposed to achieve good performance under communication errors based on the adaptive compensation of the delays and packet dropouts. The adverse impacts of communication errors are effectively reduced by a novel delay compensation scheme, where the feedback signal and the control command are compensated in each control period in the central controller and the swing motor driver, respectively, without requiring additional network bandwidth. The recursive least-squares algorithm with forgetting factor algorithm is employed to identify the time-varying model parameters due to pose variation, and a reverse correction law is embedded into the feedback compensation in consecutive packet dropout scenarios to overcome the impacts of the model error. Simulations and practical experiments are conducted. The results show that the proposed fault-tolerant strategy can effectively reduce the communication-error-induced overshoot and response time variation.展开更多
Using electric motors instead of diesel engines as the driving system for mining excavators can reduce the energy consumption and operating costs.However,pure electric-driven mining excavators are prone to unexpected ...Using electric motors instead of diesel engines as the driving system for mining excavators can reduce the energy consumption and operating costs.However,pure electric-driven mining excavators are prone to unexpected power outages in mines because of drastic changes in load power,leading to significant fluctuations in the power demand of the grid,which in turn affects production.To solve the above problem,a pure electric-driven mining hydraulic excavator based on electric-motor-driven swing platform and hydraulic pumps was used as the research object.Moreover,supercapacitors and DC/DC converter,as the energy storage system(ESS)adjust the output power of the grid and recover the braking kinetic energy of the swing platform.Subsequently,a novel integrated energy management strategy for a DC bus voltage predictive controller based on the power feedforward of fuzzy rules is proposed to run mining excavators efficiently and reliably.Specifically,the working modes of the ESS are determined by the DC bus voltage and state of charge(SOC)of the supercapacitor.Next,the output power of the supercapacitor and the DC bus voltage were controlled by adjusting the charging and discharging currents of the DC/DC converter using a predictive controller and fuzzy rules.In addition,a digital prototype of the excavator was verified using an original machine test.The performance of the different strategies and driven systems were analyzed using digital prototypes.The results showed that,compared with traditional excavators with diesel engines,the operational cost of the developed excavators was reduced by 54.02%.Compared to pure electric-driven excavators without an ESS,the peak power of the grid for the developed excavators was reduced by 10%.This study designed an integrated energy management strategy for a pure electric mining excavator that can regulate the power output of the grid and maintain the stability of the bus voltage and SOC of the ESS.展开更多
The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training purposes.To addres...The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training purposes.To address this issue,this study proposes a transfer learning model based on a sequence-to-sequence twodimensional(2D)convolutional long short-term memory neural network(S2SCL2D).The model can use the existing data from other adjacent similar excavations to achieve wall deflection prediction once a limited amount of monitoring data from the target excavation has been recorded.In the absence of adjacent excavation data,numerical simulation data from the target project can be employed instead.A weight update strategy is proposed to improve the prediction accuracy by integrating the stochastic gradient masking with an early stopping mechanism.To illustrate the proposed methodology,an excavation project in Hangzhou,China is adopted.The proposed deep transfer learning model,which uses either adjacent excavation data or numerical simulation data as the source domain,shows a significant improvement in performance when compared to the non-transfer learning model.Using the simulation data from the target project even leads to better prediction performance than using the actual monitoring data from other adjacent excavations.The results demonstrate that the proposed model can reasonably predict the deformation with limited data from the target project.展开更多
Deep excavations in silt strata can lead to large deformation problems,posing risks to both the excavation and adjacent structures.This study combines field monitoring with numerical simulation to investigate the unde...Deep excavations in silt strata can lead to large deformation problems,posing risks to both the excavation and adjacent structures.This study combines field monitoring with numerical simulation to investigate the underlying mechanisms and key aspects associated with large deformation problems induced by deep excavation in silt strata in Shenzhen,China.The monitoring results reveal that,due to the weak property and creep effect of the silt strata,the maximum wall deflection in the first excavated section(Section 1)exceeds its controlled value at more than 93%of measurement points,reaching a peak value of 137.46 mm.Notably,the deformation exhibits prolonged development characteristics,with the diaphragm wall deflections contributing to 39%of the overall deformation magnitude during the construction of the base slab.Subsequently,numerical simulations are carried out to analyze and assess the primary factors influencing excavation-induced deformations,following the observation of large deformations.The simulations indicate that the low strength of the silt soil is a pivotal factor that results in significant deformations.Furthermore,the flexural stiffness of the diaphragm walls exerts a notable influence on the development of deformations.To address these concerns,an optimization study of potential treatment measures was performed during the subsequent excavation of Section 2.The combined treatment approach,which comprises the reinforcement of the silt layer within the excavation and the increase in the thickness of the diaphragm walls,has been demonstrated to offer an economically superior solution for the handling of thick silt strata.This approach has the effect of reducing the lateral wall displacement by 83.1%and the ground settlement by 70.8%,thereby ensuring the safe construction of the deep excavation.展开更多
Surrounding rock deterioration and large deformation have always been a significant difficulty in designing and constructing tunnels in soft rock.The key lies in real-time perception and quantitative assessment of the...Surrounding rock deterioration and large deformation have always been a significant difficulty in designing and constructing tunnels in soft rock.The key lies in real-time perception and quantitative assessment of the damaged area around the tunnel.An in situ microseismic(MS)monitoring system is established in the plateau soft tock tunnel.This technique facilitates spatiotemporal monitoring of the rock mass's fracturing expansion and squeezing deformation,which agree well with field convergence deformation results.The formation mechanisms of progressive failure evolution of soft rock tunnels were discussed and analyzed with MS data and numerical results.The results demonstrate that:(1)Localized stress concentration and layered rock result in significant asymmetry in micro-fractures propagation in the tunnel radial section.As excavation continues,the fracture extension area extends into the deep surrounding rockmass on the east side affected by the weak bedding;(2)Tunnel excavation and long-term deformation can induce tensile shear action on the rock mass,vertical tension fractures(account for 45%)exist in deep rockmass,which play a crucial role in controlling the macroscopic failure of surrounding rock;and(3)Based on the radiated MS energy,a three-dimensional model was created to visualize the damage zone of the tunnel surrounding rock.The model depicted varying degrees of damage,and three high damage zones were identified.Generally,the depth of high damage zone ranged from 4 m to 12 m.This study may be a valuable reference for the warning and controlling of large deformations in similar projects.展开更多
In underground mining,especially in entry-type excavations,the instability of surrounding rock structures can lead to incalculable losses.As a crucial tool for stability analysis in entry-type excavations,the critical...In underground mining,especially in entry-type excavations,the instability of surrounding rock structures can lead to incalculable losses.As a crucial tool for stability analysis in entry-type excavations,the critical span graph must be updated to meet more stringent engineering requirements.Given this,this study introduces the support vector machine(SVM),along with multiple ensemble(bagging,adaptive boosting,and stacking)and optimization(Harris hawks optimization(HHO),cuckoo search(CS))techniques,to overcome the limitations of the traditional methods.The analysis indicates that the hybrid model combining SVM,bagging,and CS strategies has a good prediction performance,and its test accuracy reaches 0.86.Furthermore,the partition scheme of the critical span graph is adjusted based on the CS-BSVM model and 399 cases.Compared with previous empirical or semi-empirical methods,the new model overcomes the interference of subjective factors and possesses higher interpretability.Since relying solely on one technology cannot ensure prediction credibility,this study further introduces genetic programming(GP)and kriging interpolation techniques.The explicit expressions derived through GP can offer the stability probability value,and the kriging technique can provide interpolated definitions for two new subclasses.Finally,a prediction platform is developed based on the above three approaches,which can rapidly provide engineering feedback.展开更多
Tunnel surrounding rock(TSR)deformation exhibits time-and space-dependent behavior,making it challenging for a single prediction model to capture these characteristics over extended periods.Utilizing 8 years of TSR de...Tunnel surrounding rock(TSR)deformation exhibits time-and space-dependent behavior,making it challenging for a single prediction model to capture these characteristics over extended periods.Utilizing 8 years of TSR deformation data from the Beishan exploration tunnel(BET)test platform,the metaheuristic algorithm crested porcupine optimizer(CPO)was applied for the first time to optimize the time series of TSR deformation,and an integrated model incorporating convolutional neural network(CNN),long short-term memory network(LSTM),and attention mechanism(ATT)was proposed.This model integrates the strong feature extraction capabilities of CNN,the superior sequence prediction performance of LSTM,and the effective attention mechanism of ATT.The results show that during blasting excavation,the internal displacement of TSR exhibits a stepwise change pattern.After excavation,the internal displacement enters a phase of gradual increase,ultimately reaching a stable convergence stage.The CPO-CNN-LSTM-ATT(CPO-CLA)integrated model demonstrated excellent predictive accuracy and stability across various evaluation metrics,achieving a determination coefficient(R^(2))of 0.985.Compared to the CNN-LSTM-ATT(CLA)model,the CPO-CLA model showed a 14.1%increase in R^(2),a 61.5%decrease in root mean square error(RMSE),and a 72.9%decrease in mean absolute error(MAE).In comparison with current mainstream metaheuristic integrated models,the CPO-CLA model is better suited for predicting long-term TSR deformation.It offers high computational efficiency,accurate predictions,and expertise in optimizing large datasets.展开更多
Aiming at reducing the dust pollution during the tunneling process and improving the application efficiency of air curtain dust prevention technology,according to the changes of radial jet velocity(v_(r)),axial extrac...Aiming at reducing the dust pollution during the tunneling process and improving the application efficiency of air curtain dust prevention technology,according to the changes of radial jet velocity(v_(r)),axial extraction velocity(v_(e))and extraction distance(L)in the formation process of air curtain,the numerical simulation method was used to analyze the rules of airflow structure evolution and the diffusion characteristics of dust particles in fully mechanized excavation tunnel.The results indicate that as v_(r) and v_(e) increase,the migration path of the wall jet of the air curtain changes into an axial direction;as L decreases,the migration distance increases accordingly.These phenomena make the airflow distribution in the working face tends to be uniform.The dust diffusion distance reduces as well,wherein,the range of the discrete area of dust particles decreases sharply,until all dust particles are concentrated in the accumulation area.On this basis,the v_(r),v_(e) and L were optimized and applied in the 63_(up) 08 fully mechanized working face.By the application of the optimal parameters,the average dust removal efficiency at the driver’s position increased by 71%.The dust concentration was reduced and the working environment had been improved effectively.展开更多
Accurate prediction of ground surface settlement(GSS)adjacent to an excavation is important to prevent potential damage to the surrounding environment.Previous studies have extensively delved into this topic but all u...Accurate prediction of ground surface settlement(GSS)adjacent to an excavation is important to prevent potential damage to the surrounding environment.Previous studies have extensively delved into this topic but all under the limitations of either imprecise theories or insufficient data.In the present study,we proposed a physics-constrained neural network(PhyNN)for predicting excavation-induced GSS to fully integrate the theory of elasticity with observations and make full use of the strong fitting ability of neural networks(NNs).This model incorporates an analytical solution as an additional regularization term in the loss function to guide the training of NN.Moreover,we introduced three trainable parameters into the analytical solution so that it can be adaptively modified during the training process.The performance of the proposed PhyNN model is verified using data from a case study project.Results show that our PhyNN model achieves higher prediction accuracy,better generalization ability,and robustness than the purely data-driven NN model when confronted with data containing noise and outliers.Remarkably,by incorporating physical constraints,the admissible solution space of PhyNN is significantly narrowed,leading to a substantial reduction in the need for the amount of training data.The proposed PhyNN can be utilized as a general framework for integrating physical constraints into data-driven machine-learning models.展开更多
The performance and efficiency of hydraulic excavators heavily depend on the design and optimization of their working devices.The working device,which consists of the boom,arm,and bucket,plays a crucial role in determ...The performance and efficiency of hydraulic excavators heavily depend on the design and optimization of their working devices.The working device,which consists of the boom,arm,and bucket,plays a crucial role in determining the machine's digging capacity,stability,and overall operational efficiency.This paper presents a comprehensive study on the dynamics simulation and optimization of hydraulic excavator working devices.The paper outlines the fundamental principles of dynamic modeling,incorporating multi-body dynamics and hydraulic system analysis.It further explores various simulation techniques to evaluate the performance of the working device under varying operational conditions,including load and hydraulic system effects.The study also addresses performance optimization,focusing on multi-objective optimization methods that balance multiple factors such as energy efficiency,speed,and load capacity.Additionally,the paper discusses key factors influencing performance,such as mechanical design,material properties,and operational conditions.The results of the dynamic simulations and optimization analyses demonstrate potential improvements in operational efficiency and system stability,providing a valuable framework for the design and enhancement of hydraulic excavator working devices.展开更多
The construction of the tunnel face is a critical aspect of tunnel excavation,and its supporting equipment mainly includes drilling jumbos,arch installation trolleys,wet spraying manipulators,and anchor bolt trolleys....The construction of the tunnel face is a critical aspect of tunnel excavation,and its supporting equipment mainly includes drilling jumbos,arch installation trolleys,wet spraying manipulators,and anchor bolt trolleys.To address the issues of high construction costs and the need to replace equipment for different processes,this paper designs an economical and practical multi-functional integrated trolley based on engineering cases.This trolley is suitable for various construction methods such as full-face excavation and benching method,and integrates functions such as drilling and blasting holes,anchor bolt holes,advance grouting holes,pipe roof construction,charging,anchor bolt installation and grouting,and arch mesh installation.It reduces the number of operators,improves the tunnel working environment,lowers construction costs,and enhances construction efficiency.展开更多
Excavation-induced deformations of earth-retaining walls(ERWs)can critically affect the safety of surrounding structures,highlighting the need for reliable prediction models to support timely decision-making during co...Excavation-induced deformations of earth-retaining walls(ERWs)can critically affect the safety of surrounding structures,highlighting the need for reliable prediction models to support timely decision-making during construction.This study utilizes traditional statistical ARIMA(Auto-Regressive Integrated Moving Average)and deep learning-based LSTM(Long Short-Term Memory)models to predict earth-retaining walls deformation using inclinometer data from excavation sites and compares the predictive performance of both models.The ARIMA model demonstrates strengths in analyzing linear patterns in time-series data as it progresses over time,whereas LSTM exhibits superior capabilities in capturing complex non-linear patterns and long-term dependencies within the time series data.This research includes preprocessing of measurement data for inclinometer,performance evaluation based on various time series data lengths and input variable conditions,and demonstrates that the LSTM model offers statistically significant improvements in predictive performance over the ARIMA model.In addition,by combining LSTM with attention mechanism,attention-based LSTM(ATLSTM)is proposed to improve the short-and long-term prediction performance and solve the problem of excavation site domain change.This study presents the advantages and disadvantages of major time series analysis models for the stability evaluation of mud walls using geotechnical inclinometer data from excavation sites,and suggests that time series analysis models can be used effectively through comparative experiments.展开更多
The soil arching effect is an important factor affecting the internal load transfer of excavation-induced slopes.Physical model tests are usually used for studying the soil arching effect.Although physical model tests...The soil arching effect is an important factor affecting the internal load transfer of excavation-induced slopes.Physical model tests are usually used for studying the soil arching effect.Although physical model tests can monitor local point loads to demonstrate changes in local stresses,changes in force chains inside slopes are rarely demonstrated by physical modelling,which restricts the understanding of load transfer.To explore overall changes in stresses in slopes from a more microscopic perspective,a numerical simulation of the slope under excavation was carried out.Using built-in code and fish function programming in PFC^(3D),the slope model was developed.Monitoring areas were set up to monitor the changes in stresses and force chains during excavation.The simulation results show that excavation width affects the size of deformation area,and the deformation area expands as excavation width increases.Excavation causes load transfer and the formation of soil arching in the slope.A mechanism is proposed to explain the effect of excavation on soil arching formation and load transfer.The numerical simulation is important for revealing the load transfer of slopes during excavation,and the research results have practical value for the prevention and mitigation of landslides caused by excavation.展开更多
Relics Bridge Millennia on Silk Road By staff reporter ZHOU LIN The Lingering Recollection of Historical Events:Memories of Charms Along the Silk Road Author:Dan Qing Paperback,209 pages Published by Foreign Languages...Relics Bridge Millennia on Silk Road By staff reporter ZHOU LIN The Lingering Recollection of Historical Events:Memories of Charms Along the Silk Road Author:Dan Qing Paperback,209 pages Published by Foreign Languages Press Drawing on artifacts and ruins unearthed through archaeological excavations,this book brings the history of the ancient Silk Road to life.These relics,embedded with the memories of a vanished world,wordlessly tell stories that are both intimate and epic.展开更多
This paper proposes a longitudinal vulnerability-based analysis method to evaluate the impact of foundation pit excavation on shield tunnels,accounting for geological uncertainties.First,the shield tunnel is modeled a...This paper proposes a longitudinal vulnerability-based analysis method to evaluate the impact of foundation pit excavation on shield tunnels,accounting for geological uncertainties.First,the shield tunnel is modeled as an Euler Bernoulli beam resting on the Pasternak foundation incorporating variability in subgrade parameters along the tunnel’s length.A random analysis method using random field theory is introduced to evaluate the tunnel’s longitudinal responses to excavation.Next,a risk assessment index system is established.The normalized relative depth between the excavation and the shield tunnel is used as a risk index,while the maximum longitudinal deformation,the maximum circumferential opening,and the maximum longitudinal bending moment serve as performance indicators.Based on these,a method for analyzing the longitudinal fragility of shield tunnels under excavation-induced disturbances is proposed.Finally,the technique is applied to a case study involving a foundation pit excavation above a shield tunnel,which is the primary application scenario of this method.Vulnerability curves for different performance indicators are derived,and the effects of tunnel stiffness and subgrade stiffness on the tunnel vulnerability are explored.The results reveal significant differences in vulnerability curves depending on the performance index used.Compared to the maximum circumferential opening and the maximum longitudinal bending moment,selecting the maximum longitudinal deformation as the control index better ensures the tunnel’s usability and safety under excavation disturbances.The longitudinal vulnerability of the shield tunnel nonlinearly decreases with the increase of the tunnel stiffness and subgrade stiffness,and the subgrade stiffness has a more pronounced effect.Parametric analyses suggest that actively reinforcing the substratum is more effective on reducing the risk of tunnel failure due to adjacent excavations than passive reinforcement of the tunnel structure.展开更多
文摘Visual tracking has been widely applied in construction industry and attracted signifi-cant interests recently. Lots of research studies have adopted visual tracking techniques on the surveillance of construction workforce, project productivity and construction safety. Until now, visual tracking algorithms have gained promising performance when tracking un-articulated equipment in construction sites. However, state-of-art tracking algorithms have unguaranteed performance in tracking articulated equipment, such as backhoes and excavators. The stretching buckets and booms are the main obstacles of successfully tracking articulated equipment. In order to fill this knowledge gap, the part-based tracking algorithms are introduced in this paper for tracking articulated equipment in construction sites. The part-based tracking is able to track different parts of target equipment while using multiple tracking algorithms at the same sequence. Some existing tracking methods have been chosen according to their outstanding performance in the computer vision community. Then, the part-based algorithms were created on the basis of selected visual tracking methods and tested by real construction sequences. In this way, the tracking performance was evaluated from effectiveness and robustness aspects. Throughout the quantification analysis, the tracking performance of articulated equipment was much more improved by using the part-based tracking algorithms.
基金supported by National Natural Science Foundation of China(Grant No.50875228)
文摘Hydraulic excavator is one type of the most widely applied construction equipment for various applications mainly because of its versatility and mobility.Among the tasks performed by a hydraulic excavator,repeatable level digging or flat surface finishing may take a large percentage.Using automated functions to perform such repeatable and tedious jobs will not only greatly increase the overall productivity but more importantly also improve the operation safety.For the purpose of investigating the technology without loss of generality,this research is conducted to create a coordinate control method for the boom,arm and bucket cylinders on a hydraulic excavator to perform accurate and effective works.On the basis of the kinematic analysis of the excavator linkage system,the tip trajectory of the end-effector can be determined in terms of three hydraulic cylinders coordinated motion with a visualized method.The coordination of those hydraulic cylinders is realized by controlling three electro-hydraulic proportional valves coordinately.Therefore,the complex control algorithm of a hydraulic excavator can be simplified into coordinated motion control of three individual systems.This coordinate control algorithm was validated on a wheeled hydraulic excavator,and the validation results indicated that this developed control method could satisfactorily accomplish the auto-digging function for level digging or flat surface finishing.
文摘The determination of digging resistance is of decisive importance when evaluating the mining process of digging-wheel excavators. Methodic of measuring and determinant relations （formulae） are different in various countries and by the comparison of them, underlying data for complex and objective evaluation using the knowledge from the field of rock disintegration can be obtained. In virtue of analysis of many measurements in situ, rather high specific power consumption with a wheel of excavator has been proved. What was the criterion for evaluation was the power consumption related to the volume of a rock disintegrated per unit time. From theoretical analyses, possibilities of eliminating a negative influence of the human factor upon the mining process and thus possibilities of optimum utilizing the power input of a wheel drive follow.
基金supported by the National Natural Science Foundation of China(Grant Nos.52222810 and 52178383).
文摘Dynamic disturbances with various frequencies could trigger different failure modes of deep excavations.Superimposed on this static stress are dynamic disturbances due to various dynamic vibrations,e.g.excavation blasting,blasting,tunnel boring machine(TBM)vibration,rockburst wave,earthquakes.Specifically,these dynamic sources are characterized by a wide range of wave frequencies f,resulting in differences in failure modes.A series of true-triaxial compression tests were conducted on granite to simulate the excavation-induced stress path in three-dimensional(3D)stresses.Subsequently,a dynamic disturbance with various frequencies was applied to a cuboid specimen,to reveal the behavior associated with brittle failure.The dynamic disturbance with frequencies f of 5 Hz,10 Hz,and 40 Hz generates less disturbed energy components in the granite together with higher peak strength.However,dynamic disturbances with f of 20 Hz and 30 Hz resulted in a lower peak strength;the peak strength of the rock increases sp albeit it decreases at first,then increases.This U-shaped phenomenon relates to the natural frequency of the granite under such stress conditions.Different rock lithologies consisting of diverse mineral composition,respond differently to each sensitive resonance frequency.Interestingly,the weak disturbance stress with a high frequency f and low amplitude A increases the ratio of crack damage to peak strength(scd/sp)in the granite.This leads to the inhibition of the expansion of the granite during the dynamic disturbance process.Multiple penetrating tensileeshear cracks appear in the s3-direction as the disturbance frequency f increases.
基金Ministry of Education-Singapore,Grant/Award Number:RG143/23。
文摘Grouting with water–cement mixtures is the most widely used and cost-effective method for managing excess water inflow during tunnel construction.Due to uncertain geological and hydrological conditions,current grouting design relies heavily on the experience of onsite engineers.Recent advances in machine learning offer a promising alternative to traditional design to predict grout volume and improve grouting efficiency.Here,an artificial neural network(ANN)model was developed using the data set from an operation tunnel of Jurong Rock Caverns in Singapore to showcase an efficient and physics-guided training strategy.The ANN model was refined by incorporating the spatial scenarios,including the number of grouting holes in four quadrants of tunneling faces,the sequence of grouting screens along the tunnel axis,and the order of grouting rounds on the tunneling faces.The results indicate that an improved training strategy should encompass the grouting process,from Round 1 with grouting holes uniformly distributed around the tunnel periphery,to Round 2 with grouting holes drilled midway between neighboring first-round holes,and to Round 3 with grouting holes determined by onsite engineers.This model,trained based on the order of grouting rounds,performs better than the other models,highlighting the importance of establishing machine learning models grounded in physical principles.The finding was verified by the data set from another operation tunnel and concluded with a perspective on future grouting research.
基金the National Natural Science Foundation of China （Nos. 51475414, 51475422, and 51521064） and the National Basic Research Program （973） of China （No. 2013CB035405）
文摘A distributed fault-tolerant strategy for the controller area network based electric swing system of hybrid excavators is proposed to achieve good performance under communication errors based on the adaptive compensation of the delays and packet dropouts. The adverse impacts of communication errors are effectively reduced by a novel delay compensation scheme, where the feedback signal and the control command are compensated in each control period in the central controller and the swing motor driver, respectively, without requiring additional network bandwidth. The recursive least-squares algorithm with forgetting factor algorithm is employed to identify the time-varying model parameters due to pose variation, and a reverse correction law is embedded into the feedback compensation in consecutive packet dropout scenarios to overcome the impacts of the model error. Simulations and practical experiments are conducted. The results show that the proposed fault-tolerant strategy can effectively reduce the communication-error-induced overshoot and response time variation.
基金Supported by National Natural Science Foundation of ChinaShanxi Coalbased Low-Carbon Joint Fund(Grant No.U1910211)。
文摘Using electric motors instead of diesel engines as the driving system for mining excavators can reduce the energy consumption and operating costs.However,pure electric-driven mining excavators are prone to unexpected power outages in mines because of drastic changes in load power,leading to significant fluctuations in the power demand of the grid,which in turn affects production.To solve the above problem,a pure electric-driven mining hydraulic excavator based on electric-motor-driven swing platform and hydraulic pumps was used as the research object.Moreover,supercapacitors and DC/DC converter,as the energy storage system(ESS)adjust the output power of the grid and recover the braking kinetic energy of the swing platform.Subsequently,a novel integrated energy management strategy for a DC bus voltage predictive controller based on the power feedforward of fuzzy rules is proposed to run mining excavators efficiently and reliably.Specifically,the working modes of the ESS are determined by the DC bus voltage and state of charge(SOC)of the supercapacitor.Next,the output power of the supercapacitor and the DC bus voltage were controlled by adjusting the charging and discharging currents of the DC/DC converter using a predictive controller and fuzzy rules.In addition,a digital prototype of the excavator was verified using an original machine test.The performance of the different strategies and driven systems were analyzed using digital prototypes.The results showed that,compared with traditional excavators with diesel engines,the operational cost of the developed excavators was reduced by 54.02%.Compared to pure electric-driven excavators without an ESS,the peak power of the grid for the developed excavators was reduced by 10%.This study designed an integrated energy management strategy for a pure electric mining excavator that can regulate the power output of the grid and maintain the stability of the bus voltage and SOC of the ESS.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFC3009400)the National Natural Science Foundation of China(Grant Nos.42307218 and U2239251).
文摘The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training purposes.To address this issue,this study proposes a transfer learning model based on a sequence-to-sequence twodimensional(2D)convolutional long short-term memory neural network(S2SCL2D).The model can use the existing data from other adjacent similar excavations to achieve wall deflection prediction once a limited amount of monitoring data from the target excavation has been recorded.In the absence of adjacent excavation data,numerical simulation data from the target project can be employed instead.A weight update strategy is proposed to improve the prediction accuracy by integrating the stochastic gradient masking with an early stopping mechanism.To illustrate the proposed methodology,an excavation project in Hangzhou,China is adopted.The proposed deep transfer learning model,which uses either adjacent excavation data or numerical simulation data as the source domain,shows a significant improvement in performance when compared to the non-transfer learning model.Using the simulation data from the target project even leads to better prediction performance than using the actual monitoring data from other adjacent excavations.The results demonstrate that the proposed model can reasonably predict the deformation with limited data from the target project.
基金supported by the National Natural Science Foundation of China (Grant Nos.52008039 and 52308425)the Natural Science Foundation of Hunan Province (Grant No.2021JJ40592).
文摘Deep excavations in silt strata can lead to large deformation problems,posing risks to both the excavation and adjacent structures.This study combines field monitoring with numerical simulation to investigate the underlying mechanisms and key aspects associated with large deformation problems induced by deep excavation in silt strata in Shenzhen,China.The monitoring results reveal that,due to the weak property and creep effect of the silt strata,the maximum wall deflection in the first excavated section(Section 1)exceeds its controlled value at more than 93%of measurement points,reaching a peak value of 137.46 mm.Notably,the deformation exhibits prolonged development characteristics,with the diaphragm wall deflections contributing to 39%of the overall deformation magnitude during the construction of the base slab.Subsequently,numerical simulations are carried out to analyze and assess the primary factors influencing excavation-induced deformations,following the observation of large deformations.The simulations indicate that the low strength of the silt soil is a pivotal factor that results in significant deformations.Furthermore,the flexural stiffness of the diaphragm walls exerts a notable influence on the development of deformations.To address these concerns,an optimization study of potential treatment measures was performed during the subsequent excavation of Section 2.The combined treatment approach,which comprises the reinforcement of the silt layer within the excavation and the increase in the thickness of the diaphragm walls,has been demonstrated to offer an economically superior solution for the handling of thick silt strata.This approach has the effect of reducing the lateral wall displacement by 83.1%and the ground settlement by 70.8%,thereby ensuring the safe construction of the deep excavation.
基金the funding support from the National Natural Science Foundation of China(Grant Nos.U23A2060,42177143 and 42277461).
文摘Surrounding rock deterioration and large deformation have always been a significant difficulty in designing and constructing tunnels in soft rock.The key lies in real-time perception and quantitative assessment of the damaged area around the tunnel.An in situ microseismic(MS)monitoring system is established in the plateau soft tock tunnel.This technique facilitates spatiotemporal monitoring of the rock mass's fracturing expansion and squeezing deformation,which agree well with field convergence deformation results.The formation mechanisms of progressive failure evolution of soft rock tunnels were discussed and analyzed with MS data and numerical results.The results demonstrate that:(1)Localized stress concentration and layered rock result in significant asymmetry in micro-fractures propagation in the tunnel radial section.As excavation continues,the fracture extension area extends into the deep surrounding rockmass on the east side affected by the weak bedding;(2)Tunnel excavation and long-term deformation can induce tensile shear action on the rock mass,vertical tension fractures(account for 45%)exist in deep rockmass,which play a crucial role in controlling the macroscopic failure of surrounding rock;and(3)Based on the radiated MS energy,a three-dimensional model was created to visualize the damage zone of the tunnel surrounding rock.The model depicted varying degrees of damage,and three high damage zones were identified.Generally,the depth of high damage zone ranged from 4 m to 12 m.This study may be a valuable reference for the warning and controlling of large deformations in similar projects.
基金supported by the National Natural Science Foundation of China(Grant No.42177164)the Distinguished Youth Science Foundation of Hunan Province of China(Grant No.2022JJ10073)the Outstanding Youth Project of Hunan Provincial Department of Education,China(Grant No.23B0008).
文摘In underground mining,especially in entry-type excavations,the instability of surrounding rock structures can lead to incalculable losses.As a crucial tool for stability analysis in entry-type excavations,the critical span graph must be updated to meet more stringent engineering requirements.Given this,this study introduces the support vector machine(SVM),along with multiple ensemble(bagging,adaptive boosting,and stacking)and optimization(Harris hawks optimization(HHO),cuckoo search(CS))techniques,to overcome the limitations of the traditional methods.The analysis indicates that the hybrid model combining SVM,bagging,and CS strategies has a good prediction performance,and its test accuracy reaches 0.86.Furthermore,the partition scheme of the critical span graph is adjusted based on the CS-BSVM model and 399 cases.Compared with previous empirical or semi-empirical methods,the new model overcomes the interference of subjective factors and possesses higher interpretability.Since relying solely on one technology cannot ensure prediction credibility,this study further introduces genetic programming(GP)and kriging interpolation techniques.The explicit expressions derived through GP can offer the stability probability value,and the kriging technique can provide interpolated definitions for two new subclasses.Finally,a prediction platform is developed based on the above three approaches,which can rapidly provide engineering feedback.
基金supported by the China Atomic Energy Authority(CAEA)for China’s URL Development Program and the Geological Disposal Program(Grant No.FZ2105)the National Natural Science Foundation of China(Grant No.52278420).
文摘Tunnel surrounding rock(TSR)deformation exhibits time-and space-dependent behavior,making it challenging for a single prediction model to capture these characteristics over extended periods.Utilizing 8 years of TSR deformation data from the Beishan exploration tunnel(BET)test platform,the metaheuristic algorithm crested porcupine optimizer(CPO)was applied for the first time to optimize the time series of TSR deformation,and an integrated model incorporating convolutional neural network(CNN),long short-term memory network(LSTM),and attention mechanism(ATT)was proposed.This model integrates the strong feature extraction capabilities of CNN,the superior sequence prediction performance of LSTM,and the effective attention mechanism of ATT.The results show that during blasting excavation,the internal displacement of TSR exhibits a stepwise change pattern.After excavation,the internal displacement enters a phase of gradual increase,ultimately reaching a stable convergence stage.The CPO-CNN-LSTM-ATT(CPO-CLA)integrated model demonstrated excellent predictive accuracy and stability across various evaluation metrics,achieving a determination coefficient(R^(2))of 0.985.Compared to the CNN-LSTM-ATT(CLA)model,the CPO-CLA model showed a 14.1%increase in R^(2),a 61.5%decrease in root mean square error(RMSE),and a 72.9%decrease in mean absolute error(MAE).In comparison with current mainstream metaheuristic integrated models,the CPO-CLA model is better suited for predicting long-term TSR deformation.It offers high computational efficiency,accurate predictions,and expertise in optimizing large datasets.
基金financially supported by the Natural Science Foundation of Shandong Province(ZR2020QE124,ZR2023ME031 and ZR2023ME012)Innovation Achievement Cultivation Project of Qingdao University of Technology(CLZ2022-002)National Natural Science Foundation of China(52404222 and 52374209).
文摘Aiming at reducing the dust pollution during the tunneling process and improving the application efficiency of air curtain dust prevention technology,according to the changes of radial jet velocity(v_(r)),axial extraction velocity(v_(e))and extraction distance(L)in the formation process of air curtain,the numerical simulation method was used to analyze the rules of airflow structure evolution and the diffusion characteristics of dust particles in fully mechanized excavation tunnel.The results indicate that as v_(r) and v_(e) increase,the migration path of the wall jet of the air curtain changes into an axial direction;as L decreases,the migration distance increases accordingly.These phenomena make the airflow distribution in the working face tends to be uniform.The dust diffusion distance reduces as well,wherein,the range of the discrete area of dust particles decreases sharply,until all dust particles are concentrated in the accumulation area.On this basis,the v_(r),v_(e) and L were optimized and applied in the 63_(up) 08 fully mechanized working face.By the application of the optimal parameters,the average dust removal efficiency at the driver’s position increased by 71%.The dust concentration was reduced and the working environment had been improved effectively.
基金funded by the National Natural Science Foundation of China(Grant No.52090082)support provided by the China Scholarship Council(Grant No.202206260206).
文摘Accurate prediction of ground surface settlement(GSS)adjacent to an excavation is important to prevent potential damage to the surrounding environment.Previous studies have extensively delved into this topic but all under the limitations of either imprecise theories or insufficient data.In the present study,we proposed a physics-constrained neural network(PhyNN)for predicting excavation-induced GSS to fully integrate the theory of elasticity with observations and make full use of the strong fitting ability of neural networks(NNs).This model incorporates an analytical solution as an additional regularization term in the loss function to guide the training of NN.Moreover,we introduced three trainable parameters into the analytical solution so that it can be adaptively modified during the training process.The performance of the proposed PhyNN model is verified using data from a case study project.Results show that our PhyNN model achieves higher prediction accuracy,better generalization ability,and robustness than the purely data-driven NN model when confronted with data containing noise and outliers.Remarkably,by incorporating physical constraints,the admissible solution space of PhyNN is significantly narrowed,leading to a substantial reduction in the need for the amount of training data.The proposed PhyNN can be utilized as a general framework for integrating physical constraints into data-driven machine-learning models.
文摘The performance and efficiency of hydraulic excavators heavily depend on the design and optimization of their working devices.The working device,which consists of the boom,arm,and bucket,plays a crucial role in determining the machine's digging capacity,stability,and overall operational efficiency.This paper presents a comprehensive study on the dynamics simulation and optimization of hydraulic excavator working devices.The paper outlines the fundamental principles of dynamic modeling,incorporating multi-body dynamics and hydraulic system analysis.It further explores various simulation techniques to evaluate the performance of the working device under varying operational conditions,including load and hydraulic system effects.The study also addresses performance optimization,focusing on multi-objective optimization methods that balance multiple factors such as energy efficiency,speed,and load capacity.Additionally,the paper discusses key factors influencing performance,such as mechanical design,material properties,and operational conditions.The results of the dynamic simulations and optimization analyses demonstrate potential improvements in operational efficiency and system stability,providing a valuable framework for the design and enhancement of hydraulic excavator working devices.
文摘The construction of the tunnel face is a critical aspect of tunnel excavation,and its supporting equipment mainly includes drilling jumbos,arch installation trolleys,wet spraying manipulators,and anchor bolt trolleys.To address the issues of high construction costs and the need to replace equipment for different processes,this paper designs an economical and practical multi-functional integrated trolley based on engineering cases.This trolley is suitable for various construction methods such as full-face excavation and benching method,and integrates functions such as drilling and blasting holes,anchor bolt holes,advance grouting holes,pipe roof construction,charging,anchor bolt installation and grouting,and arch mesh installation.It reduces the number of operators,improves the tunnel working environment,lowers construction costs,and enhances construction efficiency.
基金carried out under the KICT Research Program(Project No.20250285-001,Development of Infrastructure Disaster Prevention Technology Based on Satellites SAR)funded by the Ministry of Science and ICT.
文摘Excavation-induced deformations of earth-retaining walls(ERWs)can critically affect the safety of surrounding structures,highlighting the need for reliable prediction models to support timely decision-making during construction.This study utilizes traditional statistical ARIMA(Auto-Regressive Integrated Moving Average)and deep learning-based LSTM(Long Short-Term Memory)models to predict earth-retaining walls deformation using inclinometer data from excavation sites and compares the predictive performance of both models.The ARIMA model demonstrates strengths in analyzing linear patterns in time-series data as it progresses over time,whereas LSTM exhibits superior capabilities in capturing complex non-linear patterns and long-term dependencies within the time series data.This research includes preprocessing of measurement data for inclinometer,performance evaluation based on various time series data lengths and input variable conditions,and demonstrates that the LSTM model offers statistically significant improvements in predictive performance over the ARIMA model.In addition,by combining LSTM with attention mechanism,attention-based LSTM(ATLSTM)is proposed to improve the short-and long-term prediction performance and solve the problem of excavation site domain change.This study presents the advantages and disadvantages of major time series analysis models for the stability evaluation of mud walls using geotechnical inclinometer data from excavation sites,and suggests that time series analysis models can be used effectively through comparative experiments.
基金supported by the National Key Scientific Instrument and Equipment Development Projects of China(No.41827808)the Major Program of National Natural Science Foundation of China(No.42090055)+2 种基金the National Natural Science Foundation of China(Nos.42077268)the National Natural Science Foundation of China(No.42107194)the Fundamental Research Funds for the Central Universities(No.CUGL190810)。
文摘The soil arching effect is an important factor affecting the internal load transfer of excavation-induced slopes.Physical model tests are usually used for studying the soil arching effect.Although physical model tests can monitor local point loads to demonstrate changes in local stresses,changes in force chains inside slopes are rarely demonstrated by physical modelling,which restricts the understanding of load transfer.To explore overall changes in stresses in slopes from a more microscopic perspective,a numerical simulation of the slope under excavation was carried out.Using built-in code and fish function programming in PFC^(3D),the slope model was developed.Monitoring areas were set up to monitor the changes in stresses and force chains during excavation.The simulation results show that excavation width affects the size of deformation area,and the deformation area expands as excavation width increases.Excavation causes load transfer and the formation of soil arching in the slope.A mechanism is proposed to explain the effect of excavation on soil arching formation and load transfer.The numerical simulation is important for revealing the load transfer of slopes during excavation,and the research results have practical value for the prevention and mitigation of landslides caused by excavation.
文摘Relics Bridge Millennia on Silk Road By staff reporter ZHOU LIN The Lingering Recollection of Historical Events:Memories of Charms Along the Silk Road Author:Dan Qing Paperback,209 pages Published by Foreign Languages Press Drawing on artifacts and ruins unearthed through archaeological excavations,this book brings the history of the ancient Silk Road to life.These relics,embedded with the memories of a vanished world,wordlessly tell stories that are both intimate and epic.
基金Project(52178402) supported by the National Natural Science Foundation of China。
文摘This paper proposes a longitudinal vulnerability-based analysis method to evaluate the impact of foundation pit excavation on shield tunnels,accounting for geological uncertainties.First,the shield tunnel is modeled as an Euler Bernoulli beam resting on the Pasternak foundation incorporating variability in subgrade parameters along the tunnel’s length.A random analysis method using random field theory is introduced to evaluate the tunnel’s longitudinal responses to excavation.Next,a risk assessment index system is established.The normalized relative depth between the excavation and the shield tunnel is used as a risk index,while the maximum longitudinal deformation,the maximum circumferential opening,and the maximum longitudinal bending moment serve as performance indicators.Based on these,a method for analyzing the longitudinal fragility of shield tunnels under excavation-induced disturbances is proposed.Finally,the technique is applied to a case study involving a foundation pit excavation above a shield tunnel,which is the primary application scenario of this method.Vulnerability curves for different performance indicators are derived,and the effects of tunnel stiffness and subgrade stiffness on the tunnel vulnerability are explored.The results reveal significant differences in vulnerability curves depending on the performance index used.Compared to the maximum circumferential opening and the maximum longitudinal bending moment,selecting the maximum longitudinal deformation as the control index better ensures the tunnel’s usability and safety under excavation disturbances.The longitudinal vulnerability of the shield tunnel nonlinearly decreases with the increase of the tunnel stiffness and subgrade stiffness,and the subgrade stiffness has a more pronounced effect.Parametric analyses suggest that actively reinforcing the substratum is more effective on reducing the risk of tunnel failure due to adjacent excavations than passive reinforcement of the tunnel structure.
