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Article

Preventing Overturning of Mobile Cranes Using an Electrical Resistivity Measurement System

by
Hongseok Jang
1,
Yeonho Lee
1,
Hongseok Lee
1,
Youngtaek Cha
1,
Sungjoon Choi
1,* and
Jongkyu Park
2,*
1
Advanced Mobility System Group, Daegyeong Technology Application Division, Korea Institute of Industrial Technology, Daegu 42994, Republic of Korea
2
Department of Mechanical Engineering, Changwon National University, 20, Changwondaehak-ro, Uichang-gu, Changwon-si 51140, Republic of Korea
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2024, 14(21), 9623; https://doi.org/10.3390/app14219623
Submission received: 27 September 2024 / Revised: 16 October 2024 / Accepted: 16 October 2024 / Published: 22 October 2024
Browse Figures
Versions Notes

Abstract

:
Mobile cranes are essential for transporting heavy materials at construction sites, but their operation carries significant safety risks, particularly due to the potential for overturning accidents. These accidents can be classified into two main categories: mechanical accidents, which are caused by factors such as outrigger failure, excessive load weight, and operator skill, and environmental accidents, which arise from ground subsidence due to groundwater and sinkholes. While numerous studies have addressed the causes and prevention of mechanical accidents, there has been a lack of research focusing on the prevention of environmental accidents. This study presents the development of an Electrical Resistivity Measurement System (ERMS) designed to prevent overturning accidents caused by ground subsidence at mobile crane work sites. The ERMS, mounted on a mobile crane, continuously monitors the ground conditions in real time and predicts the likelihood of ground subsidence to prevent accidents. Unlike typical buried electrode methods, the proposed system features a foldable electrode mechanism and a water supply device, thereby making installation and removal more efficient. Furthermore, it uses a ground stability determination algorithm that qualitatively assesses soft ground conditions, which are the primary cause of ground subsidence. The performance of the ERMS was validated through comparisons with commercial equipment, and its applicability was further confirmed through field tests conducted at mobile crane installations. The ERMS is expected to significantly reduce the risk of accidents caused by ground subsidence during mobile crane operations and to contribute to enhancing overall safety in construction environments.

1. Introduction

Mobile cranes are widely used in construction for moving heavy materials to elevated positions. Their demand has increased with the growth of large-scale urbanization and the rapid construction of skyscrapers. Unlike fixed cranes, mobile cranes offer the advantage of moving relatively freely within narrow urban areas or construction sites [1,2]. They also reduce construction time by efficiently transporting heavy materials and objects that are beyond human capability. However, with the increasing scale of construction projects, there is a growing demand for the development of larger and higher-specification mobile cranes. Concurrently, rising concerns over safety incidents involving mobile cranes have highlighted the need for technological advancements to ensure safe and efficient operations, thereby preventing human casualties and property loss.
Crane accidents can be categorized into several types, including overloading, side pull, outrigger failure, hoist limitations, and upset/overturn, among others [3]. Mobile cranes are particularly at risk of accidents due to their reliance on outrigger-based installation, limited workspace, and operation on potentially unstable ground, unlike tower cranes, gantry cranes, and overhead cranes, which are installed on stable concrete pads and operate on solid gantry structures. The following literature consistently points to a high risk of overturning accidents involving mobile cranes. Milazzo (2017) analyzed global crane accidents from 2011 to 2015 and categorized them by crane type [4]. According to the literature, there were a total of 937 crane accidents during this period, with the distribution of accidents by crane type as follows: mobile cranes accounted for 71.82%, tower cranes 21.88%, barge cranes 2.45%, gantry/overhead cranes 0.85%, and other lifts/platforms 2.99%. Notably, the number of mobile crane accidents was more than 3.6 times higher than that of tower crane accidents, highlighting the necessity for preventive measures specific to mobile cranes. Furthermore, 45% of mobile crane accidents were due to overturning, indicating that addressing technical solutions to prevent overturning is a critical challenge. Mobile crane overturning was identified as the predominant cause of these accidents, attributed to outrigger structural defects, operator errors, overloading, and ground instability. Hamid (2019) analyzed 44 crane accident cases, and 24 of them involved mobile cranes, which were associated with a higher mortality rate than other types of cranes. Importantly, overturning accidents accounted for 39% of all crane-related accidents [5]. Similarly, Kim S (2022) analyzed 56 mobile crane accidents in Korea from 2015 to 2019, which resulted in 59 fatalities, 13 of which were caused by crane operators. The analysis revealed that the primary cause of these fatalities was falling accidents linked to outrigger installation defects. Although the overturning accident rate of mobile cranes was 10.7%, which is lower than that of falls (46.4%), collisions (28.6%), and mechanical failures (12.5%), the mortality rate was relatively high at 22% [6]. As a means to prevent such crane accidents, safety regulations are being strengthened in several countries [7,8]. Considering the high overturning accident rate of mobile cranes and the significant risk of fatalities when such accidents occur, developing technologies to enhance safety is essential.
Mobile crane overturning accidents can be classified into two categories: mechanical accidents, such as those caused by outrigger installation issues, overloading, and operator errors, and environmental accidents, such as ground subsidence due to sinkholes, groundwater, or other subsurface conditions. Various studies have been conducted to effectively prevent mechanical accidents and improve mobile crane safety. These include work planning that accounts for the crane’s operational space [9,10], the use of radio frequency identification, building information modeling simulation, real-time monitoring systems using linear regression models to assess working conditions and the surrounding environment [11,12,13,14,15], and control mechanisms based on mathematical models of lifts and outriggers [16,17,18]. Additional research has focused on predicting overturning by measuring outrigger loads [19,20,21], using sensors to prevent overloading [22,23], diagnosing faults and analyzing the causes of overturning [24,25], and implementing visual programs to reduce worker cognitive load [26]. These studies significantly contribute to the efficient operation of mobile cranes and help mitigate the overturning risks during operation. However, most of these studies address issues that occur during crane operation and do not sufficiently consider ground subsidence-related accidents. Therefore, there is a critical need for research aimed at predicting ground subsidence before crane operation to prevent environmental overturning accidents.
Ground stability is generally evaluated through various geophysical explorations, such as electrical resistivity tomography, seismic surveys, and borehole investigations, conducted before construction work [27]. However, the causes of ground subsidence, such as earthquakes, underground mining, groundwater extraction, and other subsurface activities, can originate far from construction sites and impact them over time [28,29,30]. In particular, the ground at construction sites often consists of sand, gravel, or other porous materials, which can give rise to hydrological corridors enabling water to infiltrate easily and cause ground subsidence. Ground subsidence is not a random event but rather a predictable phenomenon, and its early prediction can help prevent accidents [31,32]. However, predicting ground subsidence at construction sites is challenging, as real-time analysis of ground conditions requires time, manpower, and cost resources. Therefore, addressing this issue through targeted research and technological solutions is essential.
This study focuses on preventing overturning accidents of mobile cranes caused by ground subsidence by developing an Electrical Resistivity Measurement System (ERMS). The ERMS can be mounted on a mobile crane and allows for quick and easy measurement of ground electrical properties. Additionally, the ground safety algorithm was applied to qualitatively assess the risk of ground subsidence. The ERMS was tested under various field conditions to enhance efficiency and usability. The results demonstrate that the developed ERMS is applicable to the field and has significant potential to reduce the risk of accidents due to ground subsidence during mobile crane operations. This system is expected to be important in creating a safe working environment for mobile crane operations.

2. Objective

This study aims to prevent overturning accidents of mobile cranes by detecting ground subsidence in advance at construction sites. We developed a ground safety analysis system specifically designed for use with mobile cranes and validated its effectiveness through field tests. To enable real-time monitoring of the ground conditions, we applied an electrical resistivity exploration method [33,34]. The system used in typical electrical resistivity explorations consists of a main measurement unit, electrodes, cables, and interpretation software [33,34,35,36,37]. The development of the ERMS was based on the relevant literature. Additionally, a foldable electrode device and a wireless control program were developed to enhance user convenience and operational efficiency. The ERMS can be mounted on mobile cranes and is specifically designed to reduce the measurement time, labor, and costs associated with typical electrode installation for electrical resistivity exploration.
The focus of this study can be summarized into two key elements. First, the integration of the ERMS with mobile crane enhances worksite safety by enabling preemptive action against potential safety hazards. While previous studies have primarily focused on safety technologies applicable after the start of crane operations, the proposed system offers the advantage of preventing accidents before crane operation begins. Furthermore, the ERMS provides real-time monitoring of the ground conditions and predicts the possibility of ground subsidence, thereby helping to anticipate and prevent overturning accidents. Second, we developed a foldable electrode with a water supply device, which was designed to replace typical electrode installation methods and improve the efficiency of ground exploration. Unlike typical pin-buried electrodes, the foldable electrode device uses a cylindrical mass electrode, which is easier to install and remove. The water supply device reduces contact resistance between the electrode and ground, resulting in more accurate data acquisition. The performance of the developed ERMS was tested in the same environment using commercial equipment, including a MINISTING R1 (Advanced Geoscience Incorporation, Austin, TX, USA) and Terrameter LS2 (ABEM Instrument, Sundbyberg, Sweden). The reliability of the ERMS was confirmed by comparing the error rates of the measurement data from these commercial devices. Moreover, the ERMS was mounted on a mobile crane, and its applicability was further validated through field tests.

3. Methodology

3.1. Geophysical Exploration Methods

Geophysical exploration methods are primarily used to assess and analyze subsurface ground conditions, as illustrated in Figure 1. These methods include seismic refraction, ground-penetrating radar, electrical resistivity measurement, borehole drilling, gravity surveys, magnetic surveys, and electromagnetic methods, all of which are described in detail in reference [33]. To put it simply, Seismic refraction measures the travel time of seismic waves through subsurface materials, offering insights into the layering and material properties below the surface. Ground-penetrating radar uses high-frequency electromagnetic waves to generate images of the subsurface and provides the detection of voids, pipes, and material composition changes. Electrical resistivity measurement involves injecting electrical currents into the ground and measuring resistivity, which helps infer the soil’s composition and water content. Borehole drilling provides direct access to subsurface layers, enabling accurate geological and hydrological analysis. Gravity and magnetic surveys detect variations in gravitational and magnetic fields to map subsurface features, such as voids or mineral deposits. Finally, electromagnetic methods measure the ground’s response to induced electromagnetic fields to map conductivity variations, helping to identify different subsurface materials.
According to John M., the most advantageous geophysical exploration methods for locating groundwater and detecting cavities are electrical resistivity (ER) measurement, electromagnetic (EM), and ground-penetrating radar (GPR) [34]. These methods are favored for their simple measurement processes and portability. EM and GPR methods typically use a frequency range between 10 MHz and 2.6 GHz. Therefore, these methods provide higher resolution than the electrical resistivity method, but the penetration depth of waves to the underground is limited to less than about 3~5 m due to the very high attenuation of electromagnetic waves [38,39,40]. Moreover, these methods can be challenging to operate on rough or curved ground surfaces, which are typical of construction sites. In contrast, the ER method, which uses electrode burial, is suitable for use on uneven ground surfaces at construction sites and consumes less power than the EM and GPR methods, allowing for more compact equipment design. The ER method can probe hundreds of meters underground, and the acquired data can be visualized to provide an intuitive understanding of subsurface conditions [41]. However, the ER method is susceptible to noise from buried pipes, wires, buildings, and natural potentials, which can compromise data reliability; therefore, caution is required when interpreting the results.
The principle of the ER method involves placing four electrodes in a straight line on the ground surface and measuring the electrical resistivity using two current and two potential electrodes. The resistance values vary based on the electrical characteristics of the underground medium, and the measurement depth and resolution depend on the electrode configuration [42]. Common electrode array configurations include Wenner, Schlumberger, and dipole–dipole arrays [43], which are used for one-dimensional (1D) vertical, two-dimensional (2D), three-dimensional (3D), and four-dimensional (4D) explorations with time intervals [44,45]. The basic ER system comprises a data acquisition device, electrodes, and a controller, with additional software required for data inversion and imaging.
In this study, the ER system was developed with considerations of equipment miniaturization, the ground environment of construction site, and the exploration depth required for integration with mobile cranes. The Wenner array configuration, which allows for equal spacing of electrodes, was selected to facilitate ease of installation by workers.

3.2. Electrical Resistivity Measurement System

Electrical resistivity measurement equipment generally consists of a power supply, ammeter, voltmeter, driving module, and electrodes (Figure 2a). The system uses four electrodes for current supply and potential measurements, with multiple buried electrodes collecting data via switching control. The lower part of Figure 2b illustrates the pseudo-depth level of the Wenner array [46]. The pseudo-depth level indicates the measurement position based on the electrode array configuration in the electrical resistivity apparatus. In an ideal semi-infinite ground, the pseudo-depth corresponds to an equal interval length of the AMNB. However, due to the variability in materials and structures under real ground conditions, the accuracy of the measured values may differ from that of theoretical calculations. Consequently, the resistivity measured by the equipment is expressed as the apparent resistivity.
The configuration of the developed ERMS is shown in Figure 3, which outlines the connection flow of each component. The main measuring device integrates the current source, voltage source, controller, and switching module into a single unit and includes a battery for portable field operation. The current source utilizes a current sensor, Solid-State Relay (SSR), and Analog-to-Digital Converter (ADC), while the voltage source uses an Amplifier (AMP) and Pulse Voltage Regulator (PVR). The Main Control Unit (MCU) is based on the Arduino Due and MEGA, which are 32-bit microprocessor units, and includes a Wi-Fi module for wireless communication. The switching module uses a 16-channel Multiplexer (MUX) to facilitate the automatic control of multiple electrodes. The design and development of the main measuring device were based on various studies related to electrical resistivity equipment development [47,48,49,50]. The developed system includes additional components, such as a foldable electrode device, a water supply device, wireless operation capability, and a dedicated control program, which distinguish it from typical electrical resistivity exploration equipment.
The data acquisition device was designed with the construction environment in mind, as shown in Figure 4, emphasizing resilience against external impacts, waterproofing, and dustproofing. The equipment case features a fan, battery status window, power switches, and emergency stop switches. Connectors for battery charging and electrode connection are mounted on the side, using a military-grade connector for secure fixation. The control and data calculation program is embedded in the MCU, and the measured data are transmitted wirelessly to a tablet PC. The main measuring device is operated via a graphical user interface (GUI).

3.3. Foldable Electrode with Water Supply Device

In typical electrical resistivity tomography, current is applied, and potential is measured through pin electrodes buried in the ground. As the spacing between electrodes increases, the exploration depth increases, enabling the measurement of a wider area. High-resolution data acquisition requires densely spaced electrode installation in limited exploration areas. However, this setup requires significant manual labor for electrode burial, which reduces the mobility and convenience of exploration. This study proposes methods to enhance the convenience of electrode installation. First, cylindrical mass electrodes are proposed as a replacement for typical pin electrodes that require burial. This approach is based on the flat electrode method used in electrical resistivity tomography [51,52,53,54,55], in which a cylindrical mass lightly contacts the ground surface to apply current and measure potential (Figure 5). Second, a water supply device is used to reduce the contact resistance caused by the gap between the soil and the cylindrical mass electrode. The water supply device includes a small pump, solenoid valve, spray nozzle, and switching terminal. The device functions by placing a cylindrical mass electrode and spraying water between the electrode and soil to reduce contact resistance. Third, a foldable electrode device was developed by combining a cylindrical mass electrode, a water supply device, and a measurement cable (Figure 6). This device is designed to reduce weight by using carbon pipes. The carbon pipe and links are connected to allow folding, and a magnetic clip secures the folded state for easy storage. Connectors are installed at the start, connection link, and end of the foldable electrode device to connect the cables and electrodes; additionally, a hose and spray nozzle for water supply are integrated. The foldable electrode device maintains a 1 m interval, facilitating electrode placement in the Wenner method, and can be manufactured to accommodate the desired number of electrodes. The measurement cable is embedded within the pipe, simplifying the connection to the measuring device.
Each set of the foldable electrode device consists of 8 electrodes connected by a carbon pipe, and 6 sets (3 on each side) were used. The device can be adjusted to fit the size of a mobile crane. As shown in Figure 5c, the cylindrical mass electrode was fabricated from SUS304 (stainless steel) with a screw tap on the top for connecting the wire and bolt. The cylindrical mass has dimensions of 20 mm diameter and 30 mm length and weighs 50 g. Using a Wenner array with 24 electrodes enabled measurements up to pseudo-depth level 7 (Figure 2). The developed foldable electrode device replaces the typical electrode burial method, enhancing worker convenience and allowing easy movement and installation of electrodes, thereby improving the efficiency of ground exploration work.

3.4. Underground Safety Evaluation Algorithm

To analyze the causes of ground subsidence through electrical resistivity exploration, it is essential to understand the electrical characteristics of the ground, which are influenced by various materials. Factors such as soil water content, porosity, and layered structure affect these characteristics, necessitating a thorough analysis of the measured data under different environmental conditions [56,57,58,59]. Typically, electrical resistivity exploration data are analyzed through an inversion process using mathematical modeling and numerical solutions to interpret the subsurface structure [60].
This study focuses on analyzing the causes of ground subsidence related to soft ground influenced by groundwater and small cavities, such as sinkholes. In general, from an electrical/electronic engineering perspective, areas with high water content, such as those saturated with groundwater, exhibit low electrical resistivity, whereas regions with high porosity, such as cavities, exhibit high resistivity [30]. Based on these principles, this study proposes a method for determining ground safety by identifying abnormal resistivity zones. The pseudo-depth level of the measured apparent resistivity data can be expressed as a time series, as illustrated in Figure 7. The x-axis represents the measured depth of the ground, and the y-axis indicates the electrical resistivity. An average electrical resistivity range for the measured data is established, and any abnormal resistivity zones are identified based on significant deviations of either high or low resistivity values from this average. The average resistivity range can be reliably obtained by exploring the measurement area. High loads during mobile crane operations can lead to ground subsidence, posing safety risks. Therefore, the underground safety evaluation algorithm can serve as a qualitative criterion to assess the likelihood of groundwater presence or cavities beneath the ground by observing abnormal resistivity zones using only pseudo-depth levels.

3.5. Graphical User Interface Program

A graphical user interface (GUI) program was developed to control the ERMS and analyze ground conditions in real time. The GUI for the ERMS was programed using LabWindows/CVI (VER. 2017) based on the C programing language. As shown in Figure 8, the GUI was designed to enable intuitive control of the ERMS. Through the GUI, users can sequentially check the installation status of the ERMS, verify wireless communication connections, select measurement methods, choose the water supply method, select between automatic and manual measurement modes, and define the measurement area after the mobile crane is positioned at the work site. Once these steps are completed, measurements can be initiated, and data can be monitored in real time, allowing intuitive analysis.
Table 1 shows the detailed functions of the developed GUI. The features include real-time data visualization and the ability to control electrical resistivity measurements and water supply devices automatically through the interface. The GUI legend for the electrical resistivity values references the soil resistivity distribution reported in various studies [33,34,61,62]. The GUI legend uses color to intuitively represent the safety and instability zones of the ground (Figure 8m). Areas closer to red indicate higher soil moisture content, implying the presence of soft soil that effectively conducts electricity. Conversely, areas closer to black indicate higher porosity, implying the possibility of cavity layers that do not conduct electricity. This color scheme serves as a warning, enabling mobile crane operators to immediately recognize areas with increased risks of overturning.

4. Performance TEST

4.1. Comparison of the Performance Between Cylindrical Mass and Pin Electrodes

To compare the performance of cylindrical mass electrodes and pin electrodes, tests were conducted using the same commercial equipment at the same test locations and measurement positions (Figure 9). The commercial equipment used for the tests was an AGI MINI STING R1. The test method involved performing a one-dimensional exploration using the Wenner array with four electrodes, with the distance between electrodes increasing incrementally by 1 m at equal intervals. For the cylindrical mass electrodes, water was supplied before each measurement to reduce contact resistance.
Table 2 presents the results of the one-dimensional exploration, including the average values from 10 repeated measurements. The difference between the cylindrical mass and the pin electrodes was measured to be within 3%. Based on these results, we conclude that the cylindrical mass electrode can effectively replace the pin electrode.

4.2. Comparison of 1D Exploration with ERMS and Commercial Equipment

1D exploration tests were conducted to compare the ERMS performance of a foldable electrode device and commercial equipment, measuring from the first position of each pseudo-depth level (Figure 2). As shown in Figure 10, the electrodes were installed at equal intervals using the Wenner array configuration. For the ERMS, a cylindrical mass electrode was used, while the MINISTING R1 was equipped with a pin electrode. The apparent resistivity data obtained from each piece of equipment were compared, and the values were recorded as the average of 10 repeated tests.
Table 3 presents the apparent resistivity measurement results of the ERMS and MINISTING R1. The measurement errors between the equipment were less than 2%, indicating that the manufactured ERMS can be effectively applied to 1D exploration.

4.3. Comparison of 2D Exploration with ERMS and Commercial Equipment

The 2D exploration performance test was conducted using the ERMS verified by 1D exploration. The objective of the 2D exploration performance test was to identify abnormal resistance zones characterized by rapid changes in resistance. The local area was measured using the developed equipment and compared to the larger area measured by the commercial equipment rather than matching the measurement ranges exactly. The commercial equipment used was an ABEM Terrameter LS2. The test location consisted of a uniform distribution of shale and rock (Figure 11). For the commercial equipment, electrodes were placed at 2 m intervals with a total of twenty-four electrodes, while for the ERMS, electrodes were placed at 1 m intervals with a total of twenty-four electrodes. The ERMS measurement location was centered within the electrode interval of the commercial equipment. For visual comparison of the measurement results, the commercial inversion software DIPRO (VER. 4.1) [63] was used to display the 2D data. Figure 12 shows the inversion results of the measured data. Figure 12a shows the results obtained using the commercial equipment, Figure 12b the results obtained using the ERMS, and Figure 12c the partially overlapping measurement results obtained using both the commercial equipment and ERMS. In the measurement results from the commercial equipment, a high-resistivity zone can be observed at a depth of 14–20 m along the x-direction and 6 m along the y-direction. By comparing Figure 12a,c, it can be seen that the ERMS measurement results also indicate the formation of a high-resistivity zone in a similar location to that observed with the commercial equipment.

4.4. Performance Test on Shale Ground and Coastal Ground

To further validate the performance of the ERMS, tests were conducted on shale and soft coastal ground. Since the ERMS evaluates ground safety using the pseudo-depth levels of the measured electrical resistivity data, it should be possible to visually identify abnormal resistivity zones from the electrical resistivity data displayed in the GUI. Figure 13a,c,e show the test photographs of the shale, the apparent resistivity pseudo-depth results from DIPRO, and the apparent resistivity pseudo-depth results from the ERMS, respectively. Figure 13b,d,f show test photographs of the coastal ground, the apparent resistivity pseudo-depth obtained from DIPRO, and the apparent resistivity pseudo-depth results obtained from the ERMS, respectively.
The ERMS and commercial equipment results for both shale and coastal ground exhibited similar trends. However, in the visual results, discrepancies in the legend colors between the commercial equipment and ERMS were noticeable. For instance, in the shale ground (Figure 13c,e), the legend colors differ, whereas in the coastal ground (Figure 13d,f), the ERMS legend is displayed exclusively in red compared with the commercial equipment. This difference in color representation may affect the reliability of the results.
This issue arises because commercial software automatically sets the legend based on the maximum and minimum values of the measured data, whereas the ERMS GUI uses a fixed legend setting (see Section 3.5). The ERMS is not inherently less reliable; rather, the legend is designed to be blue for hard ground and red for soft ground to facilitate differentiation between high- and low-resistivity zones. Therefore, although the ERMS provides valuable data, the GUI results for coastal ground may not adequately represent the working environment of mobile cranes due to low apparent resistivity regions.

5. Mobile Crane with Electrical Resistivity Measurement System

Finally, to integrate the verified ERMS data with a mobile crane, a 250-ton all-terrain crane (AT crane) was used. As illustrated in Figure 14, the primary measuring device and water supply device were installed in the loading space at the rear of the AT crane. The signal line connecting the foldable electrode device passed through the crane and was connected to the main measuring device. The foldable electrode device was positioned along the directions of both outriggers of the AT crane.
Figure 15 shows the application of the ERMS to the AT crane. The foldable electrode devices were arranged in three sets with a total length of 23 m, and the spacing between the foldable electrode devices was set at 6 m, accommodating the width of the AT crane. The electrode array includes 48 electrodes arranged symmetrically, with 24 placed on each side of the mobile crane. This arrangement allowed the analysis of the ground conditions beneath the outriggers on both sides of the AT crane. The ground in the test area was a landfill. The test was conducted remotely via a tablet PC after ERMS installation, including the foldable electrode.
Figure 16 presents the results of the ground condition measurements using the ERMS on the AT crane. Figure 16a,b show the apparent resistivity pseudo-depth for the right and left directions, respectively, which correspond to the position of the foldable electrode in Figure 15. Both results indicate relatively heterogeneous characteristics attributed to the heterogeneous soil properties of the landfill. The measurement area was considered unstable, and additional investigation and analysis are recommended before operating the crane.

6. Discussion

In this study, we propose an Electrical Resistivity Measurement System (ERMS) and an analytical approach to prevent overturning accidents in mobile cranes. The ERMS is more convenient than typical ground exploration methods and enables efficient and accurate analysis of ground conditions through the main measuring device and foldable electrode, including the water supply device.
One of the key elements of the ERMS is ensuring the operational safety of mobile cranes, which is addressed using an underground safety evaluation algorithm for data analysis. This algorithm is designed to analyze the risk of ground subsidence in real time, without the typical complex software inversion processes. Typical methods take considerable time to interpret data and incur software costs; however, the proposed algorithm can address these issues. The proposed method enables rapid analysis of the ground’s electrical resistivity, allowing operators to assess the potential for ground erosion or subsidence in advance.
The developed foldable electrode was tested and verified as a replacement for existing pin-buried electrodes and allows operators to perform electrode installation more conveniently and quickly. This provides significant advantages not only in terms of time savings during installation and dismantling but also in rapidly assessing ground conditions before mobile crane operations. Additionally, the foldable electrode, combined with the water-supply device, reduces contact resistance and measurement errors. It maximizes user convenience while enhancing operational safety.
An analysis of the test results indicates that the ERMS exhibits high reliability under various field conditions and is expected to play a crucial role in preventing overturning accidents caused by ground issues during mobile crane operations. This system can accurately assess ground conditions before the operation of mobile cranes, effectively preventing overturning accidents and ultimately ensuring operator safety and improving operational efficiency.
However, the ERMS controller requires separate operation from the crane controller, which causes operational inconvenience. This highlights the need for controller integration to realize efficient use. Additionally, to enhance the reliability of the ground evaluation algorithm, repeated experiments and additional data collection in various ground environments are necessary. It is also necessary to enhance the accuracy of measurement data by using various electrode array methods, such as Schlumberger and dipole–dipole arrays, in conjunction with the Wenner method. These issues highlight the need for further research that enhances the convenience and reliability of ERMSs.

7. Conclusions

Overturning accidents involving mobile cranes account for about half of all crane-related incidents, leading to severe outcomes, including casualties and property damage. These accidents are primarily caused by operational errors, such as excessive weight transfer, and ground subsidence from factors like sinkholes, groundwater, and soft soil at construction sites. While there has been significant research on preventing operational errors, studies addressing ground subsidence remain limited.
To address these issues, our research team developed an Electrical Resistivity Measurement System (ERMS) to assess ground subsidence and enhance mobile crane safety. The ERMS aims to prevent overturning by analyzing ground conditions for potential subsidence. We validated the system’s reliability by comparing it with commercial equipment, ensuring data accuracy. A graphical user interface was also developed for improved scalability and user convenience.
Field tests demonstrated the ERMS’s effectiveness alongside commercial equipment in identical environments, further validating its applicability when mounted on a mobile crane. However, integrated operation with mobile cranes, experiments across diverse environments, and enhancements using various measurement methods remain necessary. In the future, through continued research and improvements, we anticipate broader applicability in enhancing the safety of mobile crane operations.

Author Contributions

Conceptualization, S.C. and J.P.; methodology, H.J., Y.L. and H.L.; software, Y.L.; validation, H.J. and S.C.; formal analysis, Y.C.; investigation, H.J.; resources, Y.L.; data curation, Y.C.; writing—original draft preparation, H.J.; writing—review and editing, H.J., S.C. and J.P.; visualization, H.J.; supervision, S.C.; project administration, S.C.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Technology Innovation Program (20000226, Development of 250-ton All Terrain Crane with AI-based Safety and User-friendliness) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data used in this paper were collected through the design/test supported by our project. Currently, it is difficult to disclose the data for additional testing and research. In the future, we will strive to share our data by securing more data and conducting further research.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Shapira, A.; Lucko, G.; Schexnayder, C.J. Cranes for building construction projects. J. Constr. Eng. Manag. 2007, 133, 690–700. [Google Scholar] [CrossRef]
  2. Fang, Y.; Cho, Y.K.; Durso, F.; Seo, J. Assessment of operator’s situation awareness for smart operation of mobile cranes. Autom. Constr. 2018, 85, 65–75. [Google Scholar] [CrossRef]
  3. MacCollum, D. Critical hazard analysis and crane design. Prof. Saf. 1980, 24, 31–36. [Google Scholar]
  4. Milazzo, M.F.; Ancione, G.; Brkie, V.S.; Valis, D. Investigation of crane operation safety by analyzing main accident causes. In Risk, Reliability and Safety: Innovating Theory and Practice, 1st ed.; Walls, L., Revie, M., Bedford, T., Eds.; Taylor & Francis: London, UK, 2017; pp. 74–80. [Google Scholar]
  5. Hamid, A.R.A.; Azhari, R.; Zakaria, R.; Aminudin, E.; Jaya, R.P.; Nagarajan, L.; Yahya, K.; Haron, Z.; Yunus, R. Causes of crane accidents at construction sites in Malaysia. IOP Conf. Ser. Earth Environ. Sci. 2019, 220, 012028. [Google Scholar] [CrossRef]
  6. Kim, S.; Kang, C. Analysis of the complex causes of death accidents due to mobile cranes using a modified MEPS method: Focusing on South Korea. Sustainability 2022, 14, 2948. [Google Scholar] [CrossRef]
  7. Al-Humaidi, H.M.; Hadipriono, F.T. Mobile crane safe operation approach to prevent electrocution using fuzzy-set logic models. Adv. Eng. Softw. 2009, 40, 686–696. [Google Scholar] [CrossRef]
  8. Lee, J.; Phillips, I.; Lynch, Z. Causes and prevention of mobile crane-related accidents in South Korea. Int. J. Occup. Saf. Ergon. 2020, 14, 469–478. [Google Scholar] [CrossRef] [PubMed]
  9. Zhen, L.; Hosein, T.; Ulrich, H.; Mohamed, A.H. A methodology for mobile crane lift path checking in heavy industrial projects. Autom. Constr. 2013, 31, 41–53. [Google Scholar]
  10. Safouhi, H.; Mouattamid, M.; Hermann, U.; Hendi, A. An algorithm for the calculation of feasible mobile crane position areas. Autom. Constr. 2011, 20, 360–367. [Google Scholar] [CrossRef]
  11. Soungho, C.; Tomohiro, Y. Application of RFID technology to prevention of collision accidents with heavy equipment. Autom. Constr. 2010, 19, 368–374. [Google Scholar]
  12. Leonard, E.B.; Steven, J.L.; Erik, L. Intelligent technology for truck crane accident prevention. J. Constr. Eng. Manag. 1997, 123, 276–284. [Google Scholar]
  13. Ala, N.T.; Hosein, T.; Ali, M.; Anahita, B.; Ulrich, H. BIM-based 4D mobile crane simulation and onsite operation management. Autom. Constr. 2021, 128, 103766. [Google Scholar]
  14. Weijun, R.; Zifeng, W. Real-time anticollision system for mobile cranes during lift operations. J. Comput. Civ. Eng. 2015, 29, 04014100. [Google Scholar]
  15. Fang, Y.; Cho, Y.K.; Chen, J. A framework for real-time proactive safety assistance for mobile crane lifting operations. Autom. Constr. 2016, 72, 367–379. [Google Scholar] [CrossRef]
  16. Fukagawa, R.; Muroa, T. Alarm system to prevent the overturning of truck cranes considering possible ground failure. In Automation and Robotics in Construction XI; Elsevier: Amsterdam, The Netherlands, 1994; pp. 27–34. [Google Scholar]
  17. Guangfu, S.; Michael, K. Dynamic responses of hydraulic mobile cranes with consideration of the drive system. Mech. Mach. Theory 2003, 38, 1489–1508. [Google Scholar]
  18. Kiliçslan, S.; Balkan, T.; Ider, S.K. Tipping loads of mobile cranes with flexible booms. J. Sound Vib. 1999, 223, 645–657. [Google Scholar] [CrossRef]
  19. Romanello, G. A graphical approach for the determination of outrigger loads in mobile cranes. Mech. Based Des. Struct. Mach. 2020, 50, 767–780. [Google Scholar] [CrossRef]
  20. Tamate, S.; Suemasa, N.; Katada, T. Analyses of instability in mobile cranes due to ground penetration by outriggers. J. Constr. Eng. Manag. 2005, 131, 689–697. [Google Scholar] [CrossRef]
  21. Jeng, S.L.; Yang, C.F.; Chieng, W.H. Outrigger force measure for mobile crane safety based on linear programming optimization. Mech. Based Des. Struct. Mach. 2010, 38, 145–170. [Google Scholar] [CrossRef]
  22. Han, D.S.; Ha, J.M.; Han, G.J. Development of many-angular pin type load cell for an overload limiter of a movable crane. Key Eng. Mater. 2009, 413, 291–298. [Google Scholar]
  23. Noh, S.H.; Lee, Y.S.; Kim, S.H.; Cho, J.S.; Han, C.S.; Lee, S.Y.; Lee, D.E. Economical auto moment limiter for preventing mobile cargo crane overload. Sensors 2020, 20, 6355. [Google Scholar] [CrossRef] [PubMed]
  24. Kargar, M.; Jahangiri, M.; Alimohammadlu, M.; Kamalinia, M.; Mirazahossieninejad, V. Risk assessment of mobile crane overturning in asymmetric tandem lifting (ATL) operation based on fuzzy fault tree analysis (FFTA). Results Eng. 2022, 16, 100755. [Google Scholar] [CrossRef]
  25. Congwen, K.; Chimay, J.A.; Fang, Y.; John, I.M. Use of analytical tools to mitigate mobile crane-related failures. Constr. Res. Congr. 2018, 184–195. [Google Scholar] [CrossRef]
  26. Fang, Y.; Cho, Y.K. Effectiveness analysis from a cognitive perspective for a real-time safety assistance system for mobile crane lifting operations. J. Constr. Eng. Manag. 2017, 143, 05016025. [Google Scholar] [CrossRef]
  27. Anbazhagan, P. Geotechnics for Natural and Engineered Sustainable Technologies; Springer: Singapore, 2018. [Google Scholar]
  28. Choi, S.K.; Back, S.H.; An, J.B.; Kwon, T.H. Geotechnical investigation on causes and mitigation of ground subsidence during underground structure construction. J. Korean Tunn. Undergr. Space Assoc. 2016, 18, 143–154. [Google Scholar] [CrossRef]
  29. Choi, S.O.; Jeon, Y.S.; Park, E.S.; Jung, Y.B.; Chun, D.S. Analysis of subsidence mechanism and development of evaluation program. Tunn. Undergr. Space 2005, 15, 195–212. [Google Scholar]
  30. Karoui, T.; Jeong, S.Y.; Jeong, Y.H.; Kim, D.S. Experimental study of ground subsidence mechanism caused by sewer pipe cracks. Appl. Sci. 2018, 8, 679. [Google Scholar] [CrossRef]
  31. Fetter, C.W. Applied Hydrogeology, 4th ed.; Prentice Hall: Upper Saddle River, NJ, USA, 2001; pp. 2–8. [Google Scholar]
  32. Schiavo, M. Entropy, fractality, and thermodynamics of groundwater pathways. J. Hydrol. 2023, 617, 128930. [Google Scholar] [CrossRef]
  33. Burger, H.R.; Sheehan, A.F.; Jones, C.H. Introduction to Applied Geophysics: Exploring the Shallow Subsurface; Cambridge University Press: Cambridge, UK, 2023. [Google Scholar]
  34. Reynolds, J.M. An Introduction to Applied and Environmental Geophysics; John Wiley and Sons Ltd.: Chichester, UK, 1997. [Google Scholar]
  35. Kemna, A.; Vanderborght, J.; Kulessa, B.; Vereecken, H. Imaging and characterisation of subsurface solute transport using electrical resistivity tomography (ERT) and equivalent transport models. J. Hydrol. 2002, 267, 125–146. [Google Scholar] [CrossRef]
  36. Clement, R.; Fargier, Y.; Dubois, V.; Gance, J.; Gros, E.; Forquet, N. OhmPi: An open-source data logger for dedicated applications of electrical resistivity imaging at the small and laboratory scale. HardwareX 2020, 8, e00122. [Google Scholar] [CrossRef]
  37. Ducut, J.D.; Alipio, M.; Go, P.J.; Concepcion, R., II; Vicerra, R.R.; Bandala, A.; Dadios, E. A review of electrical resistivity tomography applications in underground imaging and object detection. Displays 2022, 73, 102208. [Google Scholar] [CrossRef]
  38. Doolittle, J.A.; Brevik, E.C. The use of electromagnetic induction techniques in soil studies. Geoderma 2014, 223–225, 33–45. [Google Scholar] [CrossRef]
  39. Slob, E.; Sato, M.; Olhoeft, G. Ground-penetrating radar: Surface and borehole ground-penetrating radar developments. In Geophysics Today: A Survey of the Field as the Journal Celebrates its 75th Anniversary; Fomel, S., Ed.; SEG: Houston, TX, USA, 2010. [Google Scholar]
  40. Zajícová, K.; Chuman, T. Application of ground penetrating radar methods in civil engineering and geology. Geosciences 2021, 11, 31. [Google Scholar]
  41. Rodriguez, A.; Lázaro, M.; Sanz, M.A.; Aguirre, M.; López, E. Ground-penetrating radar technology for soil characterization and monitoring. J. Appl. Geophys. 2019, 169, 103895. [Google Scholar]
  42. D’Astous, A.; Neuman, S.P.; Lowry, S.D. Ground-penetrating radar and electrical resistivity imaging in monitoring moisture transport in a cover system. Geotech. Geol. Eng. 2018, 36, 73–89. [Google Scholar]
  43. Gurin, V.; Torgashev, A.; Torgasheva, I.; Sazhin, A.; Sazhin, D.; Badalian, A. Electrical resistivity tomography (ERT) for geotechnical applications. Geophys. J. Int. 2022, 229, 988–1006. [Google Scholar]
  44. Jha, B.K.; Koundal, M.; Kumar, S.; Sharma, K.K.; Kumar, R.; Dubey, A.K. Design and analysis of electrical resistivity tomography for geotechnical investigation of near-surface soils. Geotech. Geol. Eng. 2023, 41, 343–360. [Google Scholar]
  45. Uskov, A.V.; Hmila, H.; Khalil, A.; Kostiuchenko, S.V.; Kofman, E.M.; Zhamkov, R.A.; Sukhareva, A.G.; Nikiforov, V.I. Experimental study of resistivity tomography for detecting near-surface soil moisture. Sensors 2020, 20, 2091. [Google Scholar]
  46. Babich, A.V.; Sukhareva, A.G.; Uskov, A.V.; Nikiforov, V.I. Electrical resistivity tomography for soil moisture monitoring: A case study of the Alym river basin. Water 2021, 13, 3516. [Google Scholar]
  47. Yang, T.H.; Chen, H.C. The relationship between soil moisture and electrical resistivity in different soil conditions. Sensors 2020, 20, 6107. [Google Scholar]
  48. Bescos, C.; Martínez-García, D.; Esquius, J.; Gozalvez, J. A performance study of 2D resistivity inversion algorithms for characterization of subsurface materials: A case study. Geophys. Prospect. 2021, 69, 1986–1999. [Google Scholar]
  49. Zhao, J.; Chen, Z.; Huang, Y.; Li, Q.; Yang, Y.; Zhang, Y. Ground penetrating radar for monitoring soil moisture: A review. Water 2023, 15, 2261. [Google Scholar]
  50. Liu, J.; Zhang, S.; Zhang, Z.; Liu, G.; He, J.; Zhang, D.; Wu, Y. A novel approach to interpret ground penetrating radar data for soil moisture estimation. Remote Sens. 2022, 14, 5863. [Google Scholar]
  51. Mohamed, M.H.; Kaul, S.; Kahn, S.; Ghosh, M.; Khanna, N. A comparative study of electrical resistivity and ground penetrating radar methods for estimating soil moisture content. Sensors 2022, 22, 1234. [Google Scholar]
  52. Alsharif, A.A.; Daisuke, Y.; Kajiura, Y. A new approach for groundwater monitoring using resistivity imaging and GPR: A case study in Japan. Hydrol. Sci. J. 2022, 67, 793–810. [Google Scholar]
  53. Li, J.; Yang, Y.; Wang, Y.; Jiang, Z.; Gao, L.; Li, H. Comparison of electrical resistivity tomography and ground penetrating radar for monitoring moisture variation in unsaturated soils. Water 2023, 15, 120. [Google Scholar]
  54. Hatzopoulos, A.; Cacoub, T.; Kelaidis, A.; Natsopoulos, K. Multi-method geophysical investigation for the assessment of groundwater resources: A case study from Central Greece. Geophys. J. Int. 2022, 228, 923–936. [Google Scholar]
  55. Hurst, M.D.; Murchison, J.M.; Rivas, A.; Omari, A.; McGarry, M. Monitoring subsurface contamination using geophysical methods: Case studies in Pennsylvania and New York. Geophys. Prospect. 2022, 70, 262–279. [Google Scholar]
  56. Tsang, Y.; Hsu, S.; Li, C. Evaluation of soil moisture dynamics using geophysical methods: A case study in Taiwan. Sensors 2021, 21, 1237. [Google Scholar]
  57. Ma, Y.; Huo, X.; Zhou, H.; Cheng, Q.; Lin, L.; Wang, S. Soil moisture estimation using time-lapse electrical resistivity tomography: A case study. J. Appl. Geophys. 2021, 189, 104295. [Google Scholar]
  58. Yao, Y.; Zhao, Y.; Hu, Y.; Yu, Y.; Chen, Y. High-resolution 3D electrical resistivity imaging for mapping moisture content in a coastal aquifer: A case study. Water 2023, 15, 712. [Google Scholar]
  59. Hurst, M.D.; Dutton, A.; Krol, A.; El-Batanouny, M. Geophysical methods for groundwater exploration: A comprehensive review. Water 2022, 14, 122. [Google Scholar]
  60. Huang, J.; Liu, H.; Zhang, C.; Wang, Y.; Li, H. Geophysical methods for characterizing soil moisture content: A review of recent advances. J. Hydrol. 2022, 604, 127117. [Google Scholar]
  61. Zhang, C.; Wang, Z.; Liu, Q.; Wang, Y.; Li, Y. Advances in GPR technology for soil moisture estimation: A comprehensive review. Remote Sens. 2023, 15, 349. [Google Scholar]
  62. Wu, S.; Han, Y.; Yan, H.; Zhao, X.; Qiu, X.; Zhou, X. Monitoring soil moisture dynamics using GPR and ERT: A case study in an agricultural field. Sensors 2023, 23, 455. [Google Scholar]
  63. Korea Institute of Geoscience and Mineral Resources. DIPRO Version 4.01, Processing and Interpretation Software for Electrical Resistivity Data; Korea Institute of Geoscience and Mineral Resources: Daejeon, Republic of Korea, 2001. [Google Scholar]
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Figure 1. Geophysical exploration methods and equipment.
Figure 1. Geophysical exploration methods and equipment.
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Figure 2. General configuration of electrical resistivity exploration: (a) General components of electrical resistivity measurement equipment; (b) Pseudo-depth level of Wenner array.
Figure 2. General configuration of electrical resistivity exploration: (a) General components of electrical resistivity measurement equipment; (b) Pseudo-depth level of Wenner array.
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Figure 3. Overview of the Electrical Resistivity Measurement System.
Figure 3. Overview of the Electrical Resistivity Measurement System.
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Figure 4. Layout of the developed data acquisition device.
Figure 4. Layout of the developed data acquisition device.
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Figure 5. Pin and cylindrical mass electrodes: (a) pin electrode; (b) cylindrical mass; (c) cylindrical mass electrode.
Figure 5. Pin and cylindrical mass electrodes: (a) pin electrode; (b) cylindrical mass; (c) cylindrical mass electrode.
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Figure 6. Foldable electrode device and water supply device.
Figure 6. Foldable electrode device and water supply device.
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Figure 7. Algorithm concept on underground safety evaluation.
Figure 7. Algorithm concept on underground safety evaluation.
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Figure 8. Overview of graphical user interface program (for descriptions of (a–n), refer to Table 1).
Figure 8. Overview of graphical user interface program (for descriptions of (a–n), refer to Table 1).
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Figure 9. Installed pin electrode and mass electrode.
Figure 9. Installed pin electrode and mass electrode.
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Figure 10. 1D exploration of EMRS.
Figure 10. 1D exploration of EMRS.
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Figure 11. 2D exploration location of ERMS and commercial equipment.
Figure 11. 2D exploration location of ERMS and commercial equipment.
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Figure 12. 2D exploration results of ERMS and commercial equipment: (a) results of Terrameter LS2; (b) results of ERMS; (c) overlapping results of Terrameter LS2 and ERMS (inverted trapezoid part with blue line).
Figure 12. 2D exploration results of ERMS and commercial equipment: (a) results of Terrameter LS2; (b) results of ERMS; (c) overlapping results of Terrameter LS2 and ERMS (inverted trapezoid part with blue line).
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Figure 13. Results of ERMS performance test on shale ground and coastal ground: (a) shale ground test bed; (b) results of DIPRO S/W on shale ground; (c) results of ERMS on shale ground; (d) coastal ground test bed; (e) results of DIPRO S/W on coastal ground; (f) results of ERMS on coastal ground.
Figure 13. Results of ERMS performance test on shale ground and coastal ground: (a) shale ground test bed; (b) results of DIPRO S/W on shale ground; (c) results of ERMS on shale ground; (d) coastal ground test bed; (e) results of DIPRO S/W on coastal ground; (f) results of ERMS on coastal ground.
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Figure 14. Installation of the ERMS and electrodes mounted on an all-terrain crane.
Figure 14. Installation of the ERMS and electrodes mounted on an all-terrain crane.
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Figure 15. 250-ton AT crane with ERMS (top view).
Figure 15. 250-ton AT crane with ERMS (top view).
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Figure 16. Results of ground condition measurement of ERMS applied to AT crane: (a) apparent resistivity pseudo-depth in the right direction; (b) apparent resistivity pseudo-depth in the left direction.
Figure 16. Results of ground condition measurement of ERMS applied to AT crane: (a) apparent resistivity pseudo-depth in the right direction; (b) apparent resistivity pseudo-depth in the left direction.
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Table 1. Description of the functionality of the developed GUI.
Table 1. Description of the functionality of the developed GUI.
ItemFunctionItemFunction
(a)Check the measurement status
  • Check the working location
  • Check outrigger location
  • Check ERMS installation
(h)Check the number of the selected electrode
(b)Check the wireless communication(i)Start and save data
(c)Final confirmation(j)Save/analyze/recall
(d)Select a measurement method
  • Auto: sequential measurement from the selected test node
  • Manual: only one measurement of the selected test node
(k)Reset: Initialize variables and test nodes
(e)Select how the water supply device works
  • Auto: spray at regular intervals
  • Manual: manual dispensing at the desired time
(l) Emergency stop
(f)Settings for manual measurement
  • Peak Time: injection current time
  • Test Num.: measurement node number
(m) Electrode node and test area (left/right)
(g)Settings for automatic measurements
  • Meas. Opt.: select a measurement area
  • W/P Interval: water supply time interval
(n)System log record
  • Display results after controlling each function
  • Display real-time measurement data
Table 2. 1D exploration results of pin and cylindrical electrodes.
Table 2. 1D exploration results of pin and cylindrical electrodes.
Electrode
Spacing
[m]		Pin Electrode
Apparent Resistivity
[Ohm·m]		Cylindrical Mass Electrode
Apparent Resistivity
[Ohm·m]		Error Rate
[%]
1132.75134.631.42
2168.17167.840.20
3174.59172.701.08
4178.29174.082.36
5184.20182.071.16
6186.20185.950.13
7187.70187.520.10
8178.24178.570.19
Table 3. 1D exploration results of EMRS and MINISTING R1.
Table 3. 1D exploration results of EMRS and MINISTING R1.
Pseudo-Depth LevelERMS
Apparent Resistivity
[Ohm·m]		MINISTING R1
Apparent Resistivity
[Ohm·m]		Error Rate
[%]		Note
190.3591.631.42First point of each pseudo-depth level
2111.51109.591.72
3136.1133.961.57
4143.84145.230.97
5175.35175.660.17
6180.86178.361.38
7188.79185.961.5
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MDPI and ACS Style

Jang, H.; Lee, Y.; Lee, H.; Cha, Y.; Choi, S.; Park, J. Preventing Overturning of Mobile Cranes Using an Electrical Resistivity Measurement System. Appl. Sci. 2024, 14, 9623. https://doi.org/10.3390/app14219623

AMA Style

Jang H, Lee Y, Lee H, Cha Y, Choi S, Park J. Preventing Overturning of Mobile Cranes Using an Electrical Resistivity Measurement System. Applied Sciences. 2024; 14(21):9623. https://doi.org/10.3390/app14219623

Chicago/Turabian Style

Jang, Hongseok, Yeonho Lee, Hongseok Lee, Youngtaek Cha, Sungjoon Choi, and Jongkyu Park. 2024. "Preventing Overturning of Mobile Cranes Using an Electrical Resistivity Measurement System" Applied Sciences 14, no. 21: 9623. https://doi.org/10.3390/app14219623

APA Style

Jang, H., Lee, Y., Lee, H., Cha, Y., Choi, S., & Park, J. (2024). Preventing Overturning of Mobile Cranes Using an Electrical Resistivity Measurement System. Applied Sciences, 14(21), 9623. https://doi.org/10.3390/app14219623

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