Monitoring the Work Cycles of Earthmoving Excavators in Earthmoving Projects Using UAV Remote Sensing

Abstract

Monitoring the work cycles of earthmoving excavators is an important aspect of construction productivity assessment. Currently, the most advanced method for the recognition of work cycles is the “Stretching-Bending” Sequential Pattern (SBSP), which is based on fixed-carrier video monitoring (FC-SBSP). However, the application of this method presupposes the availability of preconstructed installation carriers to act as a surveillance camera as well as installed and commissioned surveillance systems that work in tandem with them. Obviously, this method is difficult to apply to projects with no conditions for a monitoring camera installation or which have a short construction time. This highlights the potential application of Unmanned Aerial Vehicle (UAV) remote sensing, which is flexible and mobile. Unfortunately, few studies have been conducted on the application of UAV remote sensing for the work cycle monitoring of earthmoving excavators. This research is necessary because the use of UAV remote sensing for monitoring the work cycles of earthmoving excavators can improve construction productivity and save time and costs, especially in post-disaster reconstruction projects involving harsh construction environments, and emergency projects with short construction periods. In addition, the challenges posed by UAV shaking may have to be taken into account when using the SBSP for UAV remote sensing. To this end, this study used application experiments in which stabilization processing of UAV video data was performed for UAV shaking. The application experimental results show that the work cycle performance of UAV remote-sensing-based SBSP (UAV-SBSP) for UAV video data was 2.45% and 5.36% lower in terms of precision and recall, respectively, without stabilization processing than after stabilization processing. Comparative experiments were also designed to investigate the applicability of the SBSP oriented toward UAV remote sensing. Comparative experimental results show that the same level of performance was obtained for the recognition of work cycles with the UAV-SBSP as compared with the FC-SBSP, demonstrating the good applicability of this method. Therefore, the results of this study show that UAV remote sensing enables effective monitoring of earthmoving excavator work cycles in construction sites where monitoring cameras are not available for installation, and it can be used as an alternative technology to fixed-carrier video monitoring for onsite proximity monitoring.

Dynamic Characteristics Monitoring of Large Wind Turbine Blades Based on Target-Free DSST Vision Algorithm and UAV

“…The equations are: RMSE is calculated using Equation ( 17), where n is the total number of monitoring, x v and x s are displacement data from vision monitoring and laser displacement sensors, respectively, which measure the deviation between the measured value and the reference value. ρ is calculated by Formula (18), where µ v and µ s are the average values of the two displacement trajectories, ρ varies from 0 to 1 unit, ρ = 1 represents complete correlation, and ρ = 0 means that the two recording tracks have no correlation. The calculation formula of R 2 is Formula (19), which is used to determine the matching degree of the two recorded tracks.…”

“…The obtained data using a fixed camera depend heavily on image quality, which makes it impossible to take a comprehensive picture of large structures. The image distortion caused by the zoom due to atmospheric effect, and the inability to determine the most favorable position of the required monitoring points makes it difficult to monitor the dynamic characteristics of large structures by use of cameras [18]. Because of the flexibility and convenience of UAV, it has become popular in the field of vision-based structural health monitoring.…”

Semi-Supervised Learning for Forklift Activity Recognition from Controller Area Network (CAN) Signals

“…The categorization into three main approaches and the conclusions by Sherafat et al [2] hold well also when considering papers that are not included in the review. Some exceptions are the work by Jung et al [4], using movies of construction equipment downloaded from YouTube and two papers [5,6] which used unmanned aerial vehicles (UAV) to collect vision-based data without the constraint of fixed camera positions. Additionally, an early work by Vachkov et al [3] and a recent work by Saari and Odelius [7] applied unsupervised learning techniques for MAR; whilst some research works started to use simulated data [8,9] and augment the data with known invariances [10].…”

“…The logical order of forklift activities. Some papers [5,19] refer to this as "working cycles" of equipment.…”

Pelatihan Penggunaan Uav Dalam Pengambilan Gambar Peta Topografi Pada Jurusan BKP SMKN 5 Pekanbaru

“…Drone atau PTTA merupakan salah satu teknologi yang dapat digunakan untuk mendapatkan pemetaan secara akurat, cepat, dan real time. Unmanned Aerial Vehicle (UAV) yang saat ini sangat dikenal dengan nama Drone dapat diterbangkan pada posisi sangat rendah serta dekat dengan obyek sehingga menghasilkan gambar yang memiliki resolusi tinggi, terutama pada lokasi yang cukup sulit dijangkau serta tidak mengganggu kegiatan konstruksi [6][7][8] [9]. Bisnis Konstruksi dan Properti merupakan salah satu program keahlian di SMK Negeri 5 Pekanbaru yang mempelajari seputar perkembangan bisnis bidang properti dan pembangunan.…”