On-site Experiments Research Articles

The uncertainty analysis of bridge displacement in computer vision

Computer vision is commonly employed to measure the displacement of bridge structures. However, changes in environments often lead to poor data reproducibility. The uncertainty is an important parameter associated with the results, representing the dispersion of the data. This paper mainly discusses the uncertainty of measurement utilizing computer vision. The fundamental formula of measurement is derived from the principle of the pinhole camera model, followed by calculations of the uncertainty as well as a detailed analysis and deduction of the components that affect the uncertainty of measurement (e.g. pitch angle, deviation angle, observation distance, temperature, humidity). Both laboratory and on-site experiments have shown that the uncertainty model is more applicable than error estimation. In addition, the variation law of the uncertainty is revealed under the combination of different angles, laying a solid foundation for accurate measurement in the future.

Study of polycyclic aromatic hydrocarbons accumulation in bioretention facilities and its influence on microbial community structure.

Bioretention facilities are one of the most widely used measures for urban stormwater control and utilization. In this study, the accumulation characteristics of polycyclic aromatic hydrocarbons (PAHs) in bioretention facilities and the effects of PAHs on the structure of microbial communities were explored by combining on-site monitoring and water distribution simulation experiments. The correlation between pollutant accumulation and dominant microorganisms in the bioretention systems was also clarified. The results showed that all 16 priority PAHs were detected in the bioretention facilities in the sponge city pilot area. The PAH concentrations in the soil during the non-rainy season were higher than those in the rainy season and medium- and high-ring PAHs dominated. PAHs in the study area were mainly derived from coal and biomass combustion. The potential carcinogenic risk of PAHs accumulated in the bioretention facilities in the study area was low. The microbial diversity during the non-rainy season was greater than that during the rainy season. Firmicutes, Bacteroidetes, Bacteroides, and Massilia were strongly correlated with naphthalene (NAP), pyrene (PYR), fluoranthene (FLT), and benzo[a]pyrene (BaP). According to the results of the small-scale water distribution test, the addition of PAHs had little effect on the decline in water quantity, and there was no significant regularity in the reduction of water quality including TP, NH4+-N, NO3-N, and TN. The addition of PAHs had a significant effect on the microbial community structure and an inhibitory effect on enzyme activity.

Experimental Analysis and Machine Learning of Ground Vibrations Caused by an Elevated High-Speed Railway Based on Random Forest and Bayesian Optimization

With the aim of predicting the environmental vibrations induced by an elevated high-speed railway, a machine learning method was developed by combining a random forest algorithm and Bayesian optimization, using a dataset from on-site experiments. When it comes to achieving a rapid and effective prediction of environmental vibrations, there is little research on comparisons between and verifications of different algorithms, and none on the parameter tuning and optimization of machine learning algorithms. In this paper, a field experiment is firstly carried out to measure the ground vibrations caused by high-speed trains running on a bridge, and then the environmental vibration characteristics are analyzed in view of ground accelerations and weighted vibration levels. Subsequently, three machine learning algorithms using linear regression, support vector machine, and random forest are developed using an experimental database, and their prediction performance is discussed. Finally, two optimization models for the hyperparameter set of the random forest algorithm are further compared. The results show that the integrated random forest algorithm has a higher accuracy in predicting environmental vibrations than linear regression and the support vector machine; the Bayesian optimization has an excellent performance and a high efficiency in achieving efficient and in-depth optimization of parameters and can be combined with the RF machine learning algorithm to effectively predict the environmental vibrations induced by the high-speed railway.

Preliminary Exploration of the Technology of Coal Reshaping and Replacement Mining of Abandoned Coal in Goafs

Recycling of coal resources left behind in goafs and the treatment and utilization of solid waste from mines are topical issues faced by the global coal mining industry at present. With the annual reduction of recoverable reserves of coal resources and the dependence on coal resources that are difficult to replace, the problems have become increasingly prominent, seriously limiting the development of coal resources and the implementation of environmental protection work. The mutual influence between the strength of filling materials and the size of loose coal in the CRRM process through numerical simulation experiments, laboratory rock mechanics experiments, and on-site large-scale similar simulation material experiments is explored. It is ultimately believed that selecting a filling material with a 20 cm particle size of loose coal and a 90% proportion of loose coal for 7 days can meet the requirements of the CRRM process, and this conclusion has been verified through on-site engineering scale experiments. The scientific problems faced by various links in the process of filling and replacing abandoned coal in goafs were analyzed, and improvement methods were further proposed; the technical system of filling and replacing abandoned coal was improved, and new directions for the efficient and safe recovery of abandoned coal resources worldwide in the future were developed.

CFD Model Verification and Aerodynamic Analysis in Large-Scaled Venlo Greenhouse for Tomato Cultivation

To address the challenges of climate change and food security, the establishment of smart farm complexes is necessary. While there have been numerous studies on the productivity and environmental control of individual greenhouses, research on greenhouse complexes is considerably limited. Conducting environmental studies during the design phase of these complexes poses financial constraints and practical limitations in terms of on-site experiments. To identify potential issues that may arise when developing large-scale greenhouse complexes, it is possible to utilize modeling techniques using computational fluid dynamics (CFD) to assess environmental concerns and location issues before constructing the facilities. Consequently, simulating large-scale CFD models that incorporate multiple greenhouses and atmospheric conditions simultaneously presents significant numerical challenges. The objective of this study was to design and verify the 3D CFD model for a large-scale Venlo greenhouse, where acquiring field data before construction is not feasible for designing a greenhouse complex. The verification of the CFD models was conducted using the improved grid independence test (GIT) and wall Y+ approaches. The findings revealed that a grid resolution of 0.8 m and a first-layer height of 0.04 m were suitable for developing large-scale greenhouse models, resulting in a low Root Mean Square Error (RMSE) of 3.9% and a high coefficient of determination (R2) of 0.968. This process led to a significant reduction of 38% in the number of grid cells. Subsequently, the aerodynamic characteristics and regional ventilation efficiency were analyzed in a 3D greenhouse model for developing a new large-scale greenhouse complex.

Development, analysis, and verification of an intelligent auxiliary beekeeping device mounted on a crawler transporter

The transportation of beehives poses a significant challenge in the beekeeping industry. To address the manual handling practices prevalent in the beekeeping industry and enhance the automation level, this study attempts the design and development of a mobile auxiliary beekeeping device mounted on a caterpillar vehicle for beehive transportation. Specifically, the design of the auxiliary device was completed in SolidWorks with the notion of the cylindrical mechanical arm. Subsequently, a dynamic model of the simulated environment is constructed using RecurDyn, facilitating the determination of the torque requirements for each joint based on the dynamic model. Furthermore, the multi-flexible body dynamics analysis is utilized to conduct a flexible analysis of the auxiliary beekeeping device’s three principal components and the maximum beehive weight, which can be safely transported, is estimated through simulation analysis. Particularly, field experiments involve the use of strain gauges to measure the stress exerted on the components of the auxiliary beekeeping device during actual beehive transportation. Additionally, it incorporates the errors and precision associated with each joint, and the planar positioning accuracy result. Meanwhile, the maximum torque required for the joints, which is about 5 N·m was determined by simulation results, affirming that the selected drive motors can meet the operational requirements. Through simulation, it is determined that the auxiliary device can handle a maximum beehive weight of 126 kg, approximately three times the weight of the standard beehives (40 kg) utilized in beekeeping farms. This ensured that the auxiliary device poses no risk of overturning during the beehive transportation. In addition, the stress levels at crucial locations are ascertained through on-site strain experiments. Interestingly, the auxiliary support part (57.68 MPa) and the spindle connection (58.8 MPa) experienced the highest stress levels; and the stress levels in the telescopic rod and lower support part remained below 5 MPa. In particular, these stress levels fall within the permissible range of the materials employed (200 MPa), which ensures the auxiliary device’s safe utilization. An evaluation of each joint’s operational accuracy showed that the planar positioning accuracy result of joint 1 and joint 2 is 9.65 mm for 50% CEP and 23.55 mm for 95% CEP, respectively. Furthermore, the highest final error was exhibited by the vertical extension joint (11.48 mm), indicating that the auxiliary device can successfully accomplish the beehive transportation tasks within beekeeping farms. Overall, this research provides a solid foundation for the subsequent development of automated beehive handling devices.

Voluntary-Redundant Hybrid Control of SuperLimb Based on Redundant Muscle for On-Site Assembly Tasks

On-Site assembly is an important and complex task in many industrial fields, and it usually needs the cooperation of two skilled people. SuperLimb enhances the user's capabilities by increasing the number of limbs and provides the possibility for one person to perform on-site assembly tasks. However, explicit control from redundant body signals is difficult to control multiple degrees of freedom (DoFs) required for on-site assembly tasks due to limited control instructions. To address this issue, this paper proposes a Voluntary-Redundant hybrid control (VRHC) to achieve multiple DoFs control of SuperLimb by integrating the user's voluntary movement and redundant muscle signals. Nine intuitive redundant muscle patterns (IRMPs) are identified by a designed convolutional neural network and the muscle-velocity mapping method based on the redundant muscle force is proposed to control the velocity of the SuperLimb. The results show that the average accuracy of the IRMPs recognition is 94.03% and the average root-mean-square error with an ideal velocity-tracking curve of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sin {x}$</tex-math></inline-formula> is 11.6% of the maximum velocity. Moreover, on-site assembly experiments are conducted to verify the coupling performance of redundant muscle control and voluntary movement. Consequently, the proposed method provides an intuitive velocity control with multiple DoFs for the SuperLimb to assist the user in complex coordination tasks.

Judgment of blast furnace iron-tapping status based on data differential processing and dynamic window analysis algorithm

Ironmaking plants hold a core position in steel production. Efficient level detection and iron-tapping status judgment can accurately measure the liquid level and predict the amount of molten iron in the ladle, thereby ensuring production safety and improving production efficiency. At present, the judgment of the start and end statuses of blast furnace iron-tapping mainly relies on manual visual inspection. However, due to the high-temperature environment on-site and interference from iron powder, gunning mud, and other dust, operators find it challenging to make accurate judgments. To address these issues, this study proposes a robust and accurate method for judging the iron-tapping status of blast furnaces. This study collected four million on-site liquid level data, underwent interpolation, noise reduction, normalization, and other preprocessing steps, and then used differential processing to calculate the iron-tapping rate. Combined with dynamic window feature analysis, it effectively captured the change characteristics of the liquid level in the ladle under different iron-tapping statuses. In simulated on-site experiments, the method achieved a 96.6 ​% accuracy rate for iron-tapping status judgment. The real-time iron-tapping status judgment method based on differential processing of liquid level data and dynamic window feature analysis proposed in this study demonstrates high precision and robustness in the complex environment of ironmaking plants. This technology not only improves the production efficiency of the iron and steel link but also frees on-site operators from the harsh working environment, eliminating human errors in judgments and omissions, further promoting the intelligent process of ironmaking plants.

Vibratory compaction properties of off-shore gravity quays rubble bed based on rigid blocks

This paper intends to identify the safety and stability issues within quay structures, including scouring and hollowing, in the infrastructure for off-shore rubble bed. The three main research approaches are to combine 3D laser scanning technology, particle discrete element method and on-site experiments. The rubble bed model is established using the rigid block (Rblock) element in discrete element particle flow. Combined with the gradation generation method and the calibration of on-site tests, the influencing factors and evolution characteristics of vibratory compaction technique of rubble bed are then numerically investigated. First, the shape of the on-site stones is characterized using 3D mesoscopic extraction by 3D laser scanning technology.Statistics are used to examine the shape charactrization parameters of stone particles, and 3D mesoscopic characterization description and reconstruction techniques are developed for stone particles. Second, the 3D model of rubble bed is then built using the Rblock element in discrete particle flow, and the necessary model parameters are calibrated through a on-site vibratory compaction test. Additionally, the PFC3D program is used to study two types of influencing factors, namely the particle characteristics and vibration conditions during the vibratory compaction process of rubble bed. The results show how the riprap thickness, particle size, grading effect, vibration time, vibration frequency, and effective excitation amplitude affect the vibratory compactory properties of rubble bed. The functional link between various influencing factors with density degree and deformation modulus is proposed after combining experimental tests to examine the relevance between compaction parameters with density degree after vibration and deformation modulus. The suggested discrete element method can serve as a guide for vibratory rubble bed compaction.

Analysis of Landslide Displacement and Stress under the Condition of Overturning Failure of Fully Buried Anti-slide Piles

In this paper, on the basis of proving the basic consistency between numerical experiments and on-site physical experiments, a three-dimensional model of push pile FLAC3D was established. Based on relevant engineering analogies, parameters were selected to simulate and analyze the overturning failure mechanism of fully buried anti-slide piles. The research results indicate that the displacement of sand filling in front of the pile is significantly smaller than that of sand filling behind the pile. When approaching the failure load, a large area of uplift phenomenon appears at the top of the sliding body behind the pile; Most of the sand filling above the sliding surface showed a positive displacement along the x-axis, and the maximum displacement occurred at the top of the bulldozer. As the load increases, the sliding and upwelling area behind the pile tends to expand, and the vertical displacement at each point increases to a certain extent. At this time, the pile body is subjected to a "pull-out" effect from the surrounding sand.

T-S Fuzzy Algorithm Optimized by Genetic Algorithm for Dry Fermentation pH Control

In the process of anaerobic dry fermentation to produce biogas, maintaining a suitable pH in the environment is more conducive to the degradation of crop straw. When the pH in the fermentation environment is too low, the process of anaerobic digestion by anaerobic bacteria is inhibited. Therefore, it is necessary to quickly adjust the pH. In this work, we studied the control technology of a pH regulation system and then constructed a T-S fuzzy controller. Upon simplifying the T-S fuzzy controller, the system delay time was reduced, and two genetic algorithms with different fitness performance indicators were used to optimize the T-S fuzzy control. The simulation experiment in this study was designed through simulation software, and the results show that the improved control method has a fast regulation ability. Finally, on-site experiments were conducted using the four control methods under the acidification conditions set in the experimental device. The results show that the control method used in this study to improve performance by integrating the error sum of squares has a short control time and small oscillation and overshoot, and it can better regulate the environmental pH to achieve appropriate conditions when acidification occurs during anaerobic dry fermentation.

Key environmental factors affecting perceptions of security of night-time walking in neighbourhood streets: A discussion based on fear heat maps

BackgroundWalking plays an important role in residents’ health. After experiencing rapid development, most cities in China have gradually entered the process of urban renewal. Therefore, improving the quality of neighbourhood streets in the old city has become an important issue. The sense of security at night has become an important part of street reconstruction. Therefore, finding out the key environmental factors that affect the security of night-time walking in the streets of the old city has become an important prerequisite for street reconstruction. MethodsFirstly, according to the theoretical explanation of security in the existing literature, the environmental variables that may affect the security of night-time walking are determined. Besides, we chose the typical neighbourhood streets in the old town that need to be improved urgently, and recruited 32 subjects in an on-site experiment to record. Through wearable devices, the galvanic skin response, (GPS), and panoramic video signals generated by the subject during the experiment. The high arousal environment nodes of the subjects were determined by Galvanic skin response GSR signal and GPS signal. According to the semi-structured interview of the subjects, we valenced these high-arousal environment nodes and established a map of each subject’s fear emotional environment nodes. Aggregated into a street walking fear map by kernel density estimation. Finally, we selected the environment nodes with different degrees of fear on the map, established the optimal regression model of night-time walking security through the optimal subset regression method, and finally identified the critical environmental factors. Results and conclusionThe key environmental influences are street transparency, illegal parking level, environmental cleanliness level, light-transmittance intensity form building, CCTV density, and crowd activity intensity. The above key factors suggest that theEyes on the Street (EoS) theory seems to occupy a more important position in the relevant theories of street night-time security research. It also suggests that the privacy of subjects needs to be considered when improving the security of the street at night.

Additional pressure loss coefficient of pneumatic conveying of moist-mixed materials for shotcrete based on CFD-DEM method

A systematic study of pressure loss through four common pipeline forms for pneumatic conveying of moist-mixed materials for shotcrete (horizontal straight pipes, vertical pipes, horizontal elbow, and vertical upward elbow) was conducted using CFD-DEM coupling. On-site experiments were conducted to verify the accuracy of the simulation. The research results showed that solid-gas ratio and moist-mixed materials for shotcrete have an influence on pipeline pressure loss. Furthermore, coefficients for additional pressure loss in horizontal and vertical pipes, and an equivalent horizontal length for both types of bends were established. Additionally, the spatial position and velocity of particles were examined under four conditions. The findings of this study have implications for the design and pressure loss estimation of pneumatic conveying pipelines for moist-mixed materials for shotcrete.

Platform-independent visual installation progress monitoring for construction automation

Efficient interior progress monitoring is crucial for the timely completion of construction projects. Although robots have been used for data acquisition to automate interior progress monitoring, existing methods do not adequately consider the variations of robot platforms and different types of construction environments, which makes it challenging to apply these methods to various robots and environments. This paper proposes an integrated system that achieves automated interior installation progress monitoring, which can be applied to various construction environments and robot platforms. Algorithms are proposed to systematically generate navigation goal points for robot navigations based on BIM information of objects, which enables the detection of target objects with the proper viewing distance and angle. A transformer-based object detector is used to recognize the installation status of building elements, and a progress updating module is developed to correlate the detection results with the robot’s sensory and BIM information to generate a construction progress report for interior installation. This framework hierarchically estimates the percentage of project completion and allows for tracking of the installation work progress. The proposed system has been verified through laboratory and onsite experiments using various platforms, including a mobile robot, a four-legged robot, a drone, and a smartphone camera.

FSTNet: Learning spatial–temporal correlations from fingerprints for indoor positioning

Due to the absence of a global positioning system (GPS) signal and pervasive deployment of wireless local area networks (WLANs), Wireless Fidelity (WiFi) fingerprinting has been attracting much attention in indoor positioning services. However, accurate indoor positioning is challenging due to fingerprint ambiguity and instability problems. Most existing positioning methods do not model the spatial and temporal correlations of fingerprints, and this cannot yield satisfactory localization results. Targeting the shortcomings of existing studies, in this paper, we propose a novel deep learning framework, named FSTNet, to learn the spatial–temporal correlations from fingerprints to improve indoor positioning accuracy. In our framework, we first propose a new concept called path fingerprint to solve the fingerprint ambiguity and instability problem. Then, a convolution network is utilized to efficiently capture the local features in path fingerprints. Next, the fingerprint attention mechanism is designed to efficiently capture the spatial features and obtain stable positioning results. Finally, actual on-site experiments are conducted to verify the effectiveness of FSTNet. It is concluded that the proposed modeling and positioning method can effectively capture the temporal and spatial correlations in the received signal strength (RSS) measurements to improve positioning accuracy. In particular, the proposed model could achieve a performance improvement of 44% in terms of a mean positioning error, and 99.2% of the positioning errors are within 2 m.

The influence of the number of free surfaces on the energy distribution and attenuation law of blasting vibration signals from peripheral holes: field experiment and simulation

Tunnels are commonly excavated using drilling and blasting methods, and the surrounding rock is greatly affected by the vibration of surrounding hole blasting. To study the influence of the number of free surfaces on the energy distribution and attenuation law of surrounding hole blasting vibration signals, on-site experiments and numerical simulation experiments were conducted. The research results indicate that the higher the number of free surfaces, the smaller the peak vibration velocity. The longitudinal Fourier main frequency decreases with the distance from the monitoring point. The more free surface, the greater the centroid frequency and zero crossing frequency. In addition, numerical simulation shows that the degree of rock fragmentation after blasting increases with the increase of the number of free surface of rock.

Study on the Dynamic Characteristics of Tensional Force for Ice Accumulated Overhead Lines Considering Instantaneous Wind Speed

Icing is one of the key factors affecting the security and reliability of power system operation. Ice accumulation on the overhead line (OHL) conductors is often accompanied by strong winds, whose magnitude and direction are varied constantly, which imposes challenges to the on-line monitoring of OHL’s ice load. This paper analyzed the dynamic characteristics of the tensional force for ice accumulated OHLs through both the on-site experiment and the finite element method (FEM). The correlation between the instantaneous wind speed, the thickness of the accumulated ice and the dynamic tensional force, the aerodynamic resistance coefficient of the conductor, is investigated. It is found that the axial dynamic tension at the end of the iced conductor is approximately proportional to the square of the instantaneous wind speed. The higher the wind speed, the larger the difference between the static tension and the maximum dynamic tension calculated using the 10-min average wind speed. Under the conditions of the example in this article (crescent-shaped icing), when the average wind speed is 20 m/s, the ratio of the difference to static tension is 11.19%. These conclusions are verified with the computational fluid dynamic (CFD) simulation, the same correlation is identified in comparison with the experiments. Due to the fact that the magnitudes of tensional force and vibration are determined by the instantaneous wind speed, significant error exists within traditional calculation methods where average wind speed is used instead.

Influence of Borehole Casing on Received Signals in Downhole Method

Borehole shrinkage and collapse are likely to occur when downhole testing is conducted in soft or loose sandy soils, resulting in testing interruption. To prevent this situation from occurring, installing casing in the borehole is a common approach. However, in actual testing, the quality of the signal obtained from measuring points within the depth of the casing is often not ideal, and there is still no clear and unified justification for the causes of interference generated by the casing. Therefore, the team attempt to investigate and elucidate the impact of casing through on-site experiments and numerical simulations. Firstly, on-site tests on the impact of different materials of casing on the wave velocity test utilizing the downhole method are conducted, the waveform characteristics of the measurement points inside the PVC casing and steel casing boreholes are analyzed, and the usability of the test results are evaluated. Next, the contact state between shallow soil and casing is changed, and its impact on the waveform characteristics of signal at different depth measurement points is analyzed. Then, the ABAQUS finite element software is utilized to establish a three-dimensional finite element model for wave velocity testing using the casing method, and the dynamic response of the measuring points on the casing wall inside the hole under surface excitation is solved. By numerically simulating different casing materials, the contact state between the casing and the hole wall, and the presence of low wave velocity filling soil around the casing, the variation patterns of the affected measurement point signals in the time and frequency domains are investigated. Furthermore, combined with the measured data, the impact characteristics of the casing on the results of the wave velocity testing using the downhole method are systematically explored. This research can provide some insights for the application and data interpretation of signals in the downhole methods of cased wells.

Collaborative removal of fine catalysts particles and insoluble organics from methanol-to-olefins reaction wastewater using horizontal swirl regeneration micro-channel separation (HSRMS)

The methanol-to-olefins (MTO) process is regarded as a crucial approach for ensuring the sustainable growth of China's chemical industry. However, the fine catalyst particles and insoluble organics in the water system poses a serious threat to the stability of the MTO process. A novel horizontal swirl regeneration micro-channel separation (HSRMS) method for purifying the MTO reaction wastewater was developed in this study. On-site experiments were conducted using a device with a capacity of 1 t/h to investigate the separation and regeneration effects under various parameters and to verify the feasibility of this method through a long-term observation. The results showed that this method could effectively remove the fine solids and insoluble organics in the reaction wastewater, and the device operated stably with fluctuations in both the quality and quantity of wastewater for 24 consecutive days. The suspended solids in the effluent was maintained below 5 mg/L, and the average removal efficiency for insoluble organics exceeded 75%. Furthermore, the excellent regeneration performance was achieved, with solid particles and oil in the media reduced to less than 0.2% after regeneration. This method could ensure long-term stable operation of the water system and outperform other commonly used reaction wastewater treatment methods in terms of investment, maintenance cost, freshwater supplementation, and energy consumption.

A multicenter study on the effects of different methods of disinfecting medical external-use ultrasound probes

Microbial contamination of external-use ultrasound probes is a serious and overlooked issue. We assessed the effects of different methods of disinfecting medical external-use ultrasound probes. On-site disinfection experiments were conducted at 10 hospitals; the tips and sides of external-use ultrasound probes were sampled before and after disinfection, and 3 disinfection methods were assessed (use of a new ultraviolet [UV] ultrasound probe disinfector, wiping with ordinary paper towels or cleaning with disinfectant wipes). For the new UV probe disinfector, the median microbial death rates for the tips and sides of the external-use ultrasound probe were 93.67% and 97.50%, respectively, which were higher than those for wiping with paper towels and cleaning with disinfectant wipes (12.50% and 10.00% and 20.00% and 21.42%, respectively); the rates of microorganisms exceeding the standard were 15.0% and 13.3%, respectively, which were lower than those for wiping with paper towels and cleaning with disinfectant wipes (53.3% and 60.0% and 46.7% and 38.3%, respectively). The death rates of different species of microorganisms were high, ranging from 87.5% to 100%. The new UV ultrasound probe disinfector significantly reduced the risk of potential nosocomial infections according to the low microbial death rate for conventional disinfection methods.