Open Excavation Research Articles

Subtask-Based Usability Evaluation of Control Interfaces for Teleoperated Excavation Tasks

This study aims to experimentally determine the most suitable control interface for different subtasks in the teleoperation of construction robots in a simulation environment. We compare a conventional lever-based rate control interface (“Rate-lever”) with two alternative methods: rate control (“Rate-3D”) and position control (“Position-3D”), both using a 3D positional input device. In the experiments, participants operated a construction machine in a virtual environment and evaluated the control interfaces across three tasks: sagittal plane excavation, turning, and continuous operation. The results revealed that “Position-3D” outperformed others for sagittal excavation, while both “Rate-lever” and “Rate-3D” were more effective for turning. Notably, “Position-3D” and “Rate-3D” can be implemented on the same input device and are easily integrated. This feature offers the possibility of a hybrid-type interface suitable for operators to obtain optimized performance in sagittal and horizontal tasks.

Experimental Study on the Influence of Sidewall Excavation Width and Rock Wall Slope on the Stability of the Surrounding Rock in Hanging Tunnels

Hanging tunnels are a unique type of highway constructed on hard cliffs and towering mountains, renowned for their steep and distinctive characteristics. Compared to traditional full tunnels or open excavations, hanging tunnels offer significant advantages in terms of cost and construction time. However, the engineering design and construction cases of such tunnels are rarely reported, and concerns about construction safety and surrounding rock stability have become focal points. Taking the Shibanhe hanging tunnel as a case study, this paper focuses on the stability of the surrounding rock during the excavation of limestone hanging tunnels using physical analog model (PAM) experiments and numerical calculation. Firstly, based on the similarity principle and orthogonal experiments, river sand, bentonite, gypsum and P.O42.5 ordinary Portland cement were selected as the raw materials to configure similar materials from limestone. Secondly, according to the characteristics of hanging tunnels, geological models were designed, and excavation experiments with three different sidewall excavation widths and rock wall slopes were carried out. The effects of these variables on the stress and displacement behavior of the surrounding rock were analyzed, and the laws of their influence on the stability of the surrounding rock were explored. Finally, numerical simulations were employed to simulate the tunnel excavation, and the results of the numerical simulations and PAM experiments were compared and analyzed to verify the reliability of the PAM experiment. The results showed that the vertical stress on the rock pillars was significantly affected by the sidewall excavation widths, with a maximum increase rate of 53.8%. The displacement of the sidewall opening top was greatly influenced by the sidewall excavation widths, while the displacement of the sidewalls was more influenced by the rock wall slope. The experimental results of the PAM are consistent with the displacement and stress trends observed in the numerical simulation results, verifying their reliability. These findings can provide valuable guidance and reference for the design and construction of hanging tunnels.

Simulation of Coherent Excavator Operations in Earthmoving Tasks Based on Reinforcement Learning

Earthwork operations are critical to construction projects, with their safety and efficiency influenced by factors such as operator skill and working hours. Pre-construction simulation of these operations is essential for optimizing outcomes, providing key training for operators and improving safety awareness and operational efficiency. This study introduces a hierarchical cumulative reward mechanism that decomposes complex operational behaviors into simple, fundamental actions. The mechanism prioritizes reward function design elements, including order, size, and form, thus simplifying excavator operation simulation using reinforcement learning (RL) and enhancing policy network reusability. A 3D model of a hydraulic excavator was constructed with six degrees of freedom—comprising the boom, arm, bucket, base, and left/right tracks. The Proximal Policy Optimization (PPO) algorithm was applied to train four basic behaviors: scraping, digging, throwing, and turning back. Motion simulation was successfully achieved using diggable terrain resources. Results demonstrate that the simulated excavator, powered by RL neural networks, can perform coordinated actions and maintain smooth operational performance. This research offers practical implications by rapidly illustrating the full operational process before construction, delivering immersive movies, and enhancing worker safety and operational efficiency.

Application of 360 Camera Technology and Excavator Operator Workload at a Nickel Ore Mine and Its Implication for Work Accident Rates Using the Nasa-TLX Method

Indonesia, as one of the largest nickel producers in the world, has around 90% of nickel reserves spread across various regions, such as Central Sulawesi, South Sulawesi, Southeast Sulawesi, and North Maluku. Even though demand for nickel continues to increase, a severe problem that needs attention is the rate of work accidents in mining fields. Most heavy equipment accidents that occur are caused by operator negligence while operating. This negligence can occur when operators are in a hurry, make mistakes while using heavy equipment, or do not have proper work time management. This research aims to analyze the impact of 360-degree camera technology on excavator operators in nickel ore mines on work accident rates and operator workload. The case study was carried out at PT Rifky and Raisha Anursyah using the NASA-TLX method to measure workload. In contrast, the number of accidents that occurred to each operator within one month was used to measure work accidents. The 360 camera technology provides a comprehensive view of the work environment, allowing operators to monitor the surrounding area better and reduce blind spots. The research results show that applying this technology can reduce the risk of work accidents and increase the work efficiency of excavator operators. With better monitoring, operators can identify potential hazards earlier and reduce the risk of accidents. Workload evaluation using NASA-TLX shows a decrease in operator workload after implementing 360 camera technology. This research provides strategic recommendations for workload management and implementing safety technology in the mining industry.

Stability analysis of deep foundation pit with a double-row cast-in-place piles and diagonal steel lattice braces under sloped excavation conditions

Existing deep foundation pit support structures are commonly composed of external earth-retaining structures, internal horizontal bracings, and vertical columns. A closed bracing system, often formed by a horizontal support through a bracket board, frequently impedes vertical excavation and soil removal operations in the foundation pit, and the processes of assembly and dismantling are complex and time-consuming. This study presents a combined support system and construction method consisting of cast-in-place piles and diagonal steel lattice braces. For sloped excavation, diagonal braces were constructed by slotting through the reserved soil, allowing the use of a single layer of support within the excavation depth. This approach significantly optimizes the construction process, reducing both project duration and overall cost. The field monitoring results indicated that the support method effectively controlled the lateral displacement of the pile bodies. Field monitoring results demonstrated that the proposed support system effectively controlled the lateral displacement of the pile bodies. The adoption of a support-first, excavation-second approach significantly controlled the settlement of the ground surface around the foundation pit, thereby preventing excessive increments in the axial force of the supports due to the large longitudinal depth excavation. The calculation results of the three-dimensional finite element model for foundation pit excavation and support indicate that the proposed support method results in a decreasing ratio of the maximum lateral deformation depth of the pile body, denoted as δh-m, to the excavation depth He as the excavation depth increases. This implied that the displacement of the pile body was strictly controlled. When the depth of the foundation pit excavation exceeded 10 m, the maximum lateral deformation occurred below 10 m along the pile shaft. The diagonal steel lattice braces transferred the load to the top of the cast-in-place piles at the bottom of the pit, where the stress concentration occurred. During construction, special attention must be paid to the strength of the connection between the pile top and the connecting beams.

Evaluation of a standard analytical model and definition of efficiency metrics for lunar and planetary excavation

To sustainably live on and explore the lunar and other planetary surfaces, in situ regolith will need to be used for resource extraction and infrastructure development. To evaluate and ensure such safety and efficiency of excavation operations in variable, site-specific regolith terranes with given equipment designs, reliable computational models are necessary. The work here evaluates the accuracy and reliability of a standard, well-established excavation force prediction model using published data from a previous planetary excavation experimental study and establishes efficiency metrics for force reduced excavation as a proof-of-concept. It is shown that the established excavation force prediction model is not able to simulate planetary excavation mechanics sufficiently and hence more advanced models of lunar and planetary excavation are needed to enable safer, faster, and more economical resource acquisition hardware development. The case study of the efficiency metrics being applied to the published data shows informative trends that can be used in future excavation hardware and tool path optimizations. These are recommended for use in future planetary excavation modeling and planning workflows.

Generating realistic training images from synthetic data for excavator pose estimation

Computer vision-based 3D pose estimation for automated excavator operation monitoring requires numerous training images annotated with 3D pose labels. Owing to challenges in collecting such datasets in a field setting, using synthetic images from virtual environments has emerged recently. However, synthetic images lack the realism inherent in onsite images, potentially impacting pose estimation performance on real images. This paper thus proposes a generative model for generating realistic training excavator images with multiple backgrounds. The evaluation was conducted by comparing estimation models trained on synthetic images (Model #1), generated excavator images with single background (Model #2), and generated excavator images with multiple backgrounds (Model #3). Model #3 exhibited the lowest mean angular error of 5.96° on real data, implying its superiority in generalizing real patterns. The proposed model facilitates data acquisition for improving pose estimation without manual annotation, providing rich information on excavator movements for proactive safety and productivity management.

OPTIONS SELECTION AND IMPACT STUDY OF INTERCITY RAILWAY TUNNEL CONSTRUCTION UNDER AIRPORT INTERZONE

The selection of tunnel construction program for intercity railroad tunnel under the airport must consider the limitations of environmental conditions and the scale of tunnel construction, and the impact of construction on the airport must be controlled within safe limits. Combined with the intercity rail transit tunnel project through the airport area surrounding environment and engineering geological conditions, the use of theoretical analysis, numerical simulation and other methods, comprehensive navigation, technology, construction period, cost, construction impacts and other aspects of the comparative analysis of the tunnel construction method of open excavation method and shield method, the results show that: (1) Due to the soft soil and groundwater in the tunnel excavation area, the risk of open excavation is greater, while the shield method is more suitable for longer silty soil tunnels without affecting the navigation, accordingly, the shield tunnel construction is more suitable for the tunnel construction under the airport. (2) Open excavation method has the shortest construction period to meet the requirements of rail transit, the unit cost is between 1 and 2 shield machines, and the total cost is close to 2 shield machines; for lower total cost, choose 1 shield machine, for shortening the construction period, choose 2 shield machines. (3) The maximum ground settlement caused by shield tunneling construction is 20mm, the maximum vertical differential settlement perpendicular to the tunnel is 2.8cm, and the maximum differential settlement is 0.93 ‰. On the one hand, it meets the requirements of airport settlement control, and on the other hand, it has little impact on the vertical connecting road and west station apron, and will not affect the opening of the airport runway and the operation of the terminal.

Research on the Twin-Link Stepping Temporary Support Mechanism and the Repeatable Supportability of the Roof in the Underground Excavation Roadway of Coal Mine

The coal mine’s underground heading roadway suffers from a lack of effective temporary support, which contributes to low efficiency and inadequate safety in excavation operations. This paper presents a novel approach for roadway excavation, support, and anchoring, utilizing the twin-link stepping temporary support method. To address the stability requirements of the roadway roof in this support method, a numerical model of the excavation face was developed using the BBM (Bonded Block Model) method in 3DEC 7.0 software. Through numerical simulation, the repeatability of the twin-link stepping temporary support process was assessed for various roadway roof rock types, thicknesses, and burial depths. By considering aspects such as roof fragmentation, roof convergence, and relative displacement between the direct roof and the bedrock, the range of roadway roof repeatability was determined. The proposed twin-link stepping temporary support process, along with its applicable roof conditions, establishes a theoretical foundation for implementing the parallel operation mode in coal mine underground heading roadways, thereby significantly enhancing safety and efficiency.

Failure analysis of arch dam under fault action based on inter-generational coordination

Complex geological faults play a crucial role in the site selection and construction of dams. In this paper, considering the intergenerational coordination theory and addressing the complex geological fault conditions of the LCGX hydropower project, the entire life cycle of the arch dam is divided into four stages: initial state, dam foundation excavation, arch dam casting, and reservoir operation. Improved multiple linear regression with modified boundary conditions is employed for the inversion of initial stress, considering the unloading effects caused by dam foundation excavation and the temperature differentials arising from concrete hydration during arch dam casting, and considering the effective stress principle during the water storage operation stage. Based on the above considerations, the whole life cycle of LCGX arch dam from initial state to excavation and casting operation and maintenance is analyzed, including the impact of faults in the LCGX dam site area on the arch dam. The water pressure overload method is employed to evaluate the safety of the arch dam. And the concrete grid backfill replacement is carried out by the combination of open excavation and hole excavation. The results indicate that faults have varying degrees of influence across different stages of intergenerational coordination, with f119 and F115 showing more pronounced effects. In the overload calculation, the f119 fault, along with the hazardous rock mass formed by cutting the foundation surface, and the f124 fault, cause the arch dam to undergo displacement, thus the arch dam occurs shear failure, the reinforcement greatly improves the safety of arch dam. This study systematically investigates the arch dam and slope displacement caused by complex geological faults in the LCGX hydroelectric project dam site area. It explores the specific impact of faults on the arch dam, providing essential references for structural safety during dam maintenance.

Estimation of foam (surfactant) consumption in earth pressure balance tunnel boring machine using statistical and soft-computing methods

The use of foam, as the most economical soil conditioning technique, in earth pressure balance tunnel boring machine (EPB-TBM) tunneling projects has significant effects on operation efficiency, excavation cost, and operation time. This study mainly focuses on developing models to predict the foam (surfactant) consumption. For this purpose, five empirical models are developed based on a database containing 11048 datasets of real-time foam consumption from three EPB-TBM tunneling projects in Iran. This database includes the most effective machine operational parameters and soil geomechanical properties on the foam consumption. Multiple linear regression analysis, multiple non-linear regression analysis, M5Prime decision tree, artificial neural network, and least squares support vector machine techniques are used to construct the models. To evaluate the performance of developed models, three performance evaluation criteria (including normalized root mean square error, variance account for, and coefficient of determination) are used based on the training and testing datasets. The results show that the developed models have high performance and their validity is guaranteed according to the testing dataset. Furthermore, the M5Prime model, which demonstrates the best performance compared to other models, is applied to predict the foam consumption in 19 excavation rings of Kohandezh station in Isfahan metro, Iran. After conducting an excavation operation in this station and comparing the results, it was found that the M5Prime model accurately predicts foam consumption with an average error of less than 13%. Therefore, the developed models, particularly M5Prime model, can be confidently applied in EPB-TBM tunneling projects for predicting foam consumption with a low error rate.

CONCENTRATION OF LUNAR PLAGIOCLASE FOR SOLAR CELLS FABRICATION. AN ISRU CONCEPTUAL ARCHITECTURE

Solar panels are required on the Moon to provide power for human activities, especially mining and civil operations. To provide enough power and maintain human settlements working, a technical solution known as the Tall Lunar Tower (TLT) claims to be able to capture sunlight 93% of the time through solar panel structures and provide 50 kW per tower. A typical photovoltaic panel is made of 76% glass, 10% polymer, 8% aluminum, 5% silicon, and 1% other metals. Delivering these materials from Earth is expensive and risky. Fortunately, lunar regolith contains large amounts of silicon and aluminum oxides and silicates, thus, it would be feasible to use the resources in situ for metal production, hence, we just need to transport polymers, wire, and minor components from Earth. This article presents an ISRU (in-situ resource utilization) architecture to provide plagioclase concentrate, the economic lunar ore for aluminum and silicon extraction. The document details engineering aspects and technological solutions for lunar mining, including excavation, transport, and beneficiation operations; based on a hypothetical construction and deployment of TLT at the South Pole. Processing techniques such as screening and magnetic separation are discussed to evaluate their advantages and drawbacks to obtain an expected plagioclase concentration of 70% grade with 18% recovery. Finally, an outline of recommendations for industrial manufacture is discussed, considering the sequential lunar metals extraction and the quality required.

Recognizing sitting activities of excavator operators using multi-sensor data fusion with machine learning and deep learning algorithms

Recognizing excavator operators' sitting activities is crucial for improving their health, safety, and productivity. Moreover, it provides essential information for comprehending operators' behavior patterns and their interaction with construction equipment. However, limited research has been conducted on recognizing excavator operators' sitting activities. This paper presents a method for recognizing excavator operators' sitting activities by leveraging multi-sensor data and employing machine learning and deep learning algorithms. A multi-sensor system integrating interface pressure sensor arrays and inertial measurement units was developed to capture excavator operators' sitting activity information at a real construction site. Results suggest that the gated recurrent unit achieved outstanding performance, with 98.50% accuracy for static sitting postures and 94.25% accuracy for compound sitting actions. Moreover, several multi-sensor combination schemes were proposed to strike a balance between practicability and recognition accuracy. These findings demonstrate the feasibility and potential of the proposed approach for recognizing operators' sitting activities on construction sites.

Model-based offline reinforcement learning framework for optimizing tunnel boring machine operation

Research on automation and intelligent operation of tunnel boring machine (TBM) is receiving more and more attention, benefiting from the increasing construction data. However, most studies on TBM operations optimization were trained by the labels of human drivers’ decisions, which were subjective and stochastic. As a result, the control parameters suggested by these models could hardly surpass the performance of a human driver, even the possibility of subjective incorrect decisions. Considering that the geomechanical feedback to TBM under drivers’ actions is objective, in this paper, a transformer-based model called the geological response for tunnel boring machine (GRTBM), is proposed to learn the relationship between operation-adjust and TBM monitoring changes. Additionally, with the model-based offline reinforcement learning, this paper provided a novel approach to optimizing the TBM excavation operations. The decision processes, recorded in the Yin-song TBM project for a waterway tunnel in Jilin Province of China, were used for the validation of the model. By adopting an implicit perception of geological conditions in the GRTBM model, the suggested method achieved the desired state within a single action, greatly outperformed the practical adjustments where 500 s were taken, revealing the fact that the proposed model has the potential to surpass the capability of human beings.

Research on drilling hole sequence planning based on a double-arm drilling machine

Based on the integrated structure characteristics of the double-arm drilling machine, there are two kinds of interference between drilling and excavation operations. Through the simplified calculation of the structure model of the left and right drilling arms, the drilling range of the double-arm drilling and loading machine is obtained. Through the calculation of the structure model of the excavation arm, the excavation range is obtained. The interference area and pure interference area exist in the operation range of the three working arms. Taking the drilling arrangement of the section of the 2556 north wing transport lane in Xinji No.1 Mine as an example, the drilling hole sequence to avoid interference is planned for the 52 drilling holes in the drilling range and interference range of the left and right drilling arms, which solves the operation interference of the three working arms and plans two optimal drilling hole sequences. It provides a reference for the subsequent industrial application of various types of drilling machines in coal mines.

Groundwater potential identification using electrical resistivity tomography method in Sendangrejo Village, Minggir District, Sleman Regency, Special Region of Yogyakarta

Groundwater is vital element for living. One of methods to identify groundwater potential is Electricity Resistivity Tomography (ERT). This study aims to identify the groundwater potential occurrence in Sleman, Yogyakarta through non-destructive geoelectrical surveys. Based on the ERT models and geological information, supported by hydrogeological data in the research area, drilling a well at the planned location was deemed necessary to reach a depth of at least 100 to 250 meters in order to tap into the maximum groundwater potential. Groundwater is predicted to flow and accumulate in the pores and fractures of rocks. Well drilling in the northern part of section line 1 was more favourable and recommended due to the lower elevation compared to the area of section line 2, where the drill location was originally planned at a higher elevation uphill. Layers of hard rock, presumably lava and breccia, have already been found at a depth of 5 to 15 meters based on historical open well excavations around the area. Identifying the characteristics of subsurface volcanic rocks during drilling is suggested to evaluate drilling and well planning. Well logging should be conducted to collect lithostratigraphy and rock mass information. Further tests of hydraulic conductivity for each potential layer.

Mining Trajectory Planning of Unmanned Excavator Based on Machine Learning

Trajectory planning plays a crucial role in achieving unmanned excavator operations. The quality of trajectory planning results heavily relies on the level of rules extracted from objects such as scenes and optimization objectives, using traditional theoretical methods. To address this issue, this study focuses on professional operators and employs machine learning methods for job trajectory planning, thereby obtaining planned trajectories which exhibit excellent characteristics similar to those of professional operators. Under typical working conditions, data collection and analysis are conducted on the job trajectories of professional operators, with key points being extracted. Machine learning is then utilized to train models under different parameters in order to obtain the optimal model. To ensure sufficient samples for machine learning training, the bootstrap method is employed to adequately expand the sample size. Compared with the traditional spline curve method, the trajectories generated by machine learning models reduce the maximum speeds of excavator boom arm, dipper stick, bucket, and swing joint by 8.64 deg/s, 10.24 deg/s, 18.33 deg/s, and 1.6 deg/s, respectively; moreover, the error does not exceed 2.99 deg when compared with curves drawn by professional operators; and, finally, the trajectories generated by this model are continuously differentiable without position or velocity discontinuities, and their overall performance surpasses that of those generated by the traditional spline curve method. This paper proposes a trajectory generation method that combines excellent operators with machine learning and establishes a machine learning-based trajectory-planning model that eliminates the need for manually establishing complex rules. It is applicable to motion path planning in various working conditions of unmanned excavators.

Water inrush-induced failure during deep excavation for a metro station in karst regions in Shenzhen, China: Cause diagnosis and post-accident restoration

With the rapid development of urban underground space in karst areas, water inrush disasters during deep excavations have become widespread. However, there is a notable scarcity of comprehensive systematic studies on the causes of these disasters and environmental responses to them, and the required post-disaster restoration measures. This study investigates several water inrush disasters that occurred during the excavation for the Longdong Village Station on Shenzhen Metro Line 16, China. Through an extensive review of the geological and hydrological conditions, design and construction plan, and field monitoring data, several potential causes of the water inrush-induced failure were revealed, including i) special natural karst geology and hydrogeological conditions, ii) imprecise geological pre-investigation owing to limitations of geological prospecting technology, and iii) possibly unreasonable geological drilling and excavation operations. The magnitudes of all the monitoring items rarely varied throughout the implementation of the post-disaster restoration measures, which demonstrated the success of the measures and plans adopted after the water inrush accidents. This case provides a reference for the prevention of similar water inrush disasters and the required post-disaster restoration measures.

Bayesian Optimization for Digging Control of Wheel-Loader Using Robot Manipulator

Wheel loaders are construction machines that are mainly used for excavating and loading sedimented ground into dump trucks. The objects to be excavated range from large materials, such as blast rock and crushed stone, to small materials, such as gravel, slag, and coal ash. Therefore, the excavation operation of wheel loaders requires a high skill level to cope with various terrains and soil types. As worker numbers at quarry sites decline, developing highly automated technology to replace operators is crucial. In particular, the geometry of the ground to be excavated by the wheel loader changes with each excavation, so the control parameters must be adapted sequentially during automated excavation. In this study, we proposed an online learning method using Bayesian optimization to search for control parameters from multiple trials and modify them sequentially. In particular, we formulate a multi-objective optimization problem maximizing a weighted linear combination of the payload and workload as an objective function. To validate the proposed method, we constructed an environment in which repeated digging tests can be performed using a robot manipulator with a bucket attached. Through comparative tests between feed-forward control, in which the robot moves along a fixed trajectory independent of the digging reaction force, and off-line control, in which the robot modifies the digging trajectory in response to the digging reaction force, we compared the ability of these methods to cope with terrain volume that is different from that of the optimization trial.

Study of spatiotemporal evolution of coupled airflow–gas–dust multi-field diffusion at low-gas tunnel

The increasing level of mechanization in coal mining means more dust and gas are generated during excavation operations in tunnels. The high concentrations of dust and gas severely affect production efficiency and the physical and mental health of workers. Here, Ansys Fluent simulations were performed to derive the spatiotemporal evolution of coupled airflow–dust–gas diffusion in a low-gas excavation face. The aim was to optimize pollution control by determining the optimal duct distance, L, from the working face in the excavation tunnel. Our results showed that the airflow field affects the coupled diffusion and transport of dust and gas. According to a comparison of the effects of different duct distances from the working face, when L = 6 m, the average dust concentration in the tunnel is low (257.6 mg/m3), and the average gas concentration in the tunnel is 0.28 %, which does not exceed the safety limit. Accordingly, the optimal distance of the duct for pollution control is 6 m. The results of field measurements supported the validity of the simulation. Our findings can be used to improve the air quality in tunnels, thereby keeping miners safe and the working area clean.