Performance and cost of downhill cable yarding operations in a group shelterwood system

Munteanu, Cătălin; Yoshida, Mika; Iordache, Eugen; Borz, Stelian Alexandru; Ignea, G.

doi:10.1080/13416979.2019.1603577

Cited by 8 publications

(11 citation statements)

References 10 publications

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“…2 m/s (8 km/h, Figure 3c), results which are similar to those reported in other studies characterizing the movement speed in loaded and empty turns [12]. In other studies which approached a cable yarding configuration similar to the one in this study, movement speeds were reported to be slightly higher [28,29]. In what regards the Δc parameter, it seems that the approach of calculating the differences in course (heading) was useful in delimitating carriage movement from stopped states more clearly.…”

Section: Fig 3 Class-level Descriptive Statistics Of the Parameters U...supporting

confidence: 91%

“…2 and ending with ca. 5m/s, and all of them agreed that two classes of speed could be differentiated as empty and loaded moving of the carriage [12,13], [28,29]. Accordingly, the speed of the carriage would remain an important parameter in developing automated event recognition systems in cable yarding, mainly because it carries the information needed to distinguish between the stopped and moving states and between the two main types of carriage movements.…”

Section: Discussionmentioning

confidence: 99%

“…The yarder was rigged to extract the wood downhill, a function that was assisted by gravity, while the sledge carrying the engine was rigged near the tail tree located in the uphill end of the corridor. Although the share of using cable yarding in Romania is low [27], due to its capability of saving fuel, this configuration of the yarding system seems to be dominant in low-access steep-terrain forests, as documented also in recent studies [28,29]. The GNSS unit was set to sample locations at a rate of 0.2 Hz.…”

Section: Datamentioning

confidence: 99%

See 2 more Smart Citations

Classifying Operational Events in Cable Yarding by a Machine Learning Application to GNSS-Collected Data: A Case Study on Gravity-Assisted Downhill Yarding

Borz

Cheţa

Birda³

et al. 2022

FWIAFE

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Cable yarding remains an important option in steep terrain timber harvesting, a reason for which new or improved operational efficiency models are required to support science and practice. Developed traditionally, these models are known to require many resources, a reason for which new approaches to the problem were researched lately, mainly by the use of Global Navigation Satellite System (GNSS) data, and spatial and statistical inference systems. This study evaluates the possibility of using GNSS data and machine learning techniques to classify important cable yarding events in the time domain. Three classes were assumed by the study as being relevant for cable yarding operational setup, namely carriage moving in the uphill (MU) and downhill (MD) directions, as well as carriage stopped (S). Data collected by a consumer-grade GNSS unit was processed to extract some differential parameters which were coupled with GNSS motorial and geometric features to feed a Multi-Layer Perceptron Neural Network with Back propagation (MLPNNB) in a pre-evaluation phase which aimed at mining the data structure as a strategy to develop the best MLPNNB configuration for training and testing. Leg distance, difference in elevation, speed of the carriage, and difference in heading were used together and interchangeably in this phase, based on logical assumptions. As a result of pre-evaluation, a MLPNNB using all these datasets was found to be the best scenario. Based on this outcome, the data was split into a training (70%) and a testing (30%) subset, then the MLPNNB was used to learn and generalize on these subsets. The main results indicate that the MLPNNB had an excellent performance, with a classification accuracy of 98.7, 98.4, and 98.8% in the pre-evaluation, training, and testing phases, respectively. Log-loss errors were also found to be very low (5, 5.9, and 4.1%, respectively), indicating a high generalization capability of the MLPNNB model. Based on the results, the main conclusion of the study is that original and derived GNSS data coupled with machine learning techniques could prove to be an important tool for operational monitoring and cable yarding efficiency model development, mainly due to the possibility of working with large amounts of data.

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Section: Fig 3 Class-level Descriptive Statistics Of the Parameters U...supporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

See 1 more Smart Citation

Classifying Operational Events in Cable Yarding by a Machine Learning Application to GNSS-Collected Data: A Case Study on Gravity-Assisted Downhill Yarding

Borz

Cheţa

Birda³

et al. 2022

FWIAFE

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“…When computing the duration of the work elements, available data such as descriptive statistics and time consumption models reported in the scientific literature were used by considering the same machine. Such data were retrieved from published work by Munteanu et al [11] and its validity, particularly that of the time consumption models, was checked against other published data. Work elements such as lifting the cable at the landing, lowering the cable at the lateral yarding site, pulling in the load, lifting the load at the lateral yarding site, and lowering the load at the landing were assumed to run at an average speed of 1 m per second, which is a reasonable figure and is consistent with previous reports and practice.…”

Section: Simulation Of Noise Exposurementioning

confidence: 99%

“…The use of these machines offers several advantages, such as a lower energy use in operations and lower soil disturbance, a rather low cycle time in some operational contexts, and acceptable productivity [11]. These features make sledge yarders good candidates for ensuring the economic and environmental sustainability of forest operations [12,13].…”

Section: Introductionmentioning

confidence: 99%

A Simulation Study of Noise Exposure in Sledge-Based Cable Yarding Operations

Borz,

Mederski,

Bembenek

2024

Forests

Self Cite

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Ensuring the safety of forestry workers is a key challenge, particularly when working with partly mechanized harvesting systems. Cable yarding is typically used in steep terrain timber harvesting. For long-distance extraction, one of the few alternatives is to use sledge yarders, but these machines may expose workers to high doses of noise. The goal of this study was to model haulers’ exposure to noise in sledge-based cable yarding operations, based on a simulation approach that considered variable factors such as the yarding distance, lateral yarding distance, and average skyline height. Taken into consideration were 165 scenarios developed by examining the variation in yarding distance (500 to 1500 m, with a step of 100 m), lateral yarding distance (10 to 50 m, with a step of 10 m), and average skyline height above the ground (10, 15, and 20 m). The simulations assumed an 8-h working day with a break of 1 h. The models and statistics published by other studies were used to calculate the time consumption and number of work cycles completed within a working day. These data were used to compute the equivalent exposure to noise (LAeq) for each scenario, as well as for those work elements that were likely to expose the haulers to noise the most. The presented findings indicated that (i) the exposure to noise was higher than 100 dB(A), irrespective of variation in the considered factors; (ii) the trend in exposure was characterized by polynomials in relation to the extraction distance, and the magnitude of exposure was consistently affected by variation in the considered factors; and (iii) without hearing protection, the empty and loaded turns exposed workers to noise over the permissible limits. These findings strongly suggest the use of hearing protection when working in close proximity to sledge-based cable yarding operations. The methods proposed in this study in the form of simulation may help benchmark other forest operations.

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Tower yarder powertrain performance simulation analysis: electrification study

et al. 2023

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Radical changes are necessary to address challenges related to global warming and pollution. Ever-tightening emission standards for combustion engines have already led to a drastic reduction in the amount of harmful gas and matter emitted. Drivetrain hybridization and electrification, which are becoming increasingly popular in all sectors, are two additional ways to achieve that goal. However, within the forestry sector most of the equipments still rely on conventional mechanic or hydraulic drivetrains. An example of this is tower yarders, the workhorse of the alpine logging industry. This work simulates the duty cycle and energy flow of tower yarders in logging operations, both with conventional diesel–hydraulic configuration and a proposed hybrid configuration. The objective is to determine the potential of hybridized drivetrains for tower yarder applications. Detailed models are developed to describe the cable-based extraction of timber and tower yarder internal processes. Extensive simulations were performed to determine force, power and energy components during the harvesting operation for both the diesel–hydraulic and hybrid drivetrains. Results confirm the large potential of the hybrid configuration for efficiency improvement and emission reduction, with estimated fuel savings of 45% and 63% in the uphill and downhill configurations, respectively. Extensive sensitivity analysis further demonstrates that the hybrid concept remains effective across a wide range of cable setup and transport characteristics. This confirms the large potential of electrified drivetrains, especially in the presence of very dynamic duty cycles, as is the case in cable-based logging equipment.

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Performance and cost of downhill cable yarding operations in a group shelterwood system

Cited by 8 publications

References 10 publications

Classifying Operational Events in Cable Yarding by a Machine Learning Application to GNSS-Collected Data: A Case Study on Gravity-Assisted Downhill Yarding

Classifying Operational Events in Cable Yarding by a Machine Learning Application to GNSS-Collected Data: A Case Study on Gravity-Assisted Downhill Yarding

A Simulation Study of Noise Exposure in Sledge-Based Cable Yarding Operations

Tower yarder powertrain performance simulation analysis: electrification study

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