Types Of Construction Equipment Research Articles

Crate consolidation and standardization using lean manufacturing systems

This project was sponsored by a world-class leader in agriculture and construction equipment for nearly 200 years, hereafter referred to as Industrial Sponsor. The Sponsor’s five main brands consist of farm machinery, agriculture equipment, tractors, and various types of construction equipment. The machinery supplied by the Industrial Sponsor varies from tractors, harvesters, seeders, fertilizers, and field cultivators. For this project, a senior design team was assigned to work with the disc harrow product group at the Industrial Sponsor facility. The project scope had 3 problems: a) a large variety of crates which resulted in high pack-out times and material costs, b) an inefficient standard crate packing process due to an unorganized inventory storage area, and c) an incorrect number of parts packed in standard crates relative to the sales order. To address these problems, a variety of data sources, analysis tools, and experimental designs were employed to understand the current state, identify the risks and issues, improve the processes, and develop recommendations. Furthermore, simulations were conducted to determine the increase in efficiency based on the proposed inventory storage area. A reorganization of inventory and the use of the spaghetti diagram improved travel distance efficiency by 88 % for stationary crates and 98 % for moving crates. Yamazumi's analysis showed an improvement of 31 % in efficiency among experienced workers, compared to 0 % for new hires. The future state with a stationary crate showed an increase in efficiency of 76 % due to a significant reduction in walking distance, and the future state with a moving crate showed an increase of 86 %. In order to reduce recurring shipping errors, Kanban cards and poka-yoke systems were recommended. In addition to the variation of crates, the team identified other areas for improvement within the standard crate area, such as safety issues. This was accomplished by conducting a safety risk assessment. Finally, an implementation plan was developed to summarize and explain the process that the Industrial Sponsor would implement to fix existing inefficiencies in its crate packing operations.

Visual–auditory learning network for construction equipment action detection

AbstractAction detection of construction equipment is critical for tracking project performance, facilitating construction automation, and fostering construction efficiency in terms of construction site monitoring. Particularly, the auditory signal can provide additional information on computer vision‐based action detection of various types of construction equipment. Therefore, this study aims to develop a visual–auditory learning network model for the action detection of construction equipment based on two modalities (i.e., vision and audition). To this end, both visual and auditory features are extracted from the multi‐modal feature extractor. In addition, the multi‐head attention and detection module is designed to conduct the localization and classification tasks in separate heads in which different attention mechanisms for each task are applied. Particularly, the content‐based attention mechanism and the dot‐product attention mechanism are, respectively, adopted for spatial attention in the localization head and channel attention in the classification head. The evaluation results show that the precision and recall of the proposed model can reach 86.92% and 84.00% with the adoption of the multi‐head attention and detection module, which has proven to improve overall detection performance by utilizing different correlations of visual and auditory features for localization and classification, respectively.

Rapid Detection of Tools of Railway Works in the Full Time Domain

Construction tool detection is an important link in the operation and maintenance management of professional facilities in public works. Due to the large number and types of construction equipment and the complex and changeable construction environment, manual checking and inventory are still required. It is very challenging to count the variety of tools in a full-time environment automatically. To solve this problem, this paper aims to develop a full-time domain target detection system based on a deep learning network for difficult, complex railway environment image recognition. First, for the different time domain images, the image enhancement network with brightness channel decision is used to set different processing weights according to the images in different time domains to ensure the robustness of image enhancement in the entire time domain. Then, in view of the collected complex environment and the overlapping placement of the construction tools, a lightweight attention module is added on the basis of YOLOX, which makes the detection more purposeful, and the features cover more parts of the object to be recognized to improve the model. Overall detection performance. At the same time, the CIOU loss function is used to consider the distance fully, overlap rate, and penalty between the two detection frames, which is reflected in the final detection results, which can bring more stable target frame regression and further improve the recognition accuracy of the model. Experiments on the railway engineering dataset show that our RYOLO achieves a mAP of 77.26% for multiple tools and a count frame rate of 32.25FPS. Compared with YOLOX, mAP increased by 3.16%, especially the AP of woven bags with a high overlap rate increased from 0.15 to 0.57. Therefore, the target detection system proposed in this paper has better environmental adaptability and higher detection accuracy in complex railway environments, which is of great significance to the development of railway engineering intelligence.

Strategy for Motor Grader Blade Rotation considering Soil Distribution

Research on the automation of many types of construction equipment, including motor graders, is being actively conducted. In a motor grader cabin, the operator has difficulty observing the working environment because of a constructed field of view. Thus, workers rely on their experience and senses. Further, the working environment of the blade must be observed, and a control algorithm should be created to enable autonomous operation. In this study, a blade rotation control strategy considering the soil distribution was proposed. First, a co-simulation environment was constructed using RecurDyn for multibody dynamics analysis and EDEM for discrete element method simulation, and simulations were performed to determine the correlation between soil distribution and the blade rotation angle. Work quality and blade load were analyzed according to the simulation results. The optimal blade rotation angle according to soil distribution was obtained to develop the strategy for autonomous flattening and scattering work. The proposed control strategy was implemented in a 1/4 full-scale motor grader experimental setup. An experiment to evaluate work quality was conducted to validate the effectiveness of the proposed methods. The experimental results indicated that the proposed strategy effectively performed scattering work.

3D convolutional neural network‐based one‐stage model for real‐time action detection in video of construction equipment

AbstractThis study aims to propose a three‐dimensional convolutional neural network (3D CNN)‐based one‐stage model for real‐time action detection in video of construction equipment (ADVICE). The 3D CNN‐based single‐stream feature extraction network and detection network are designed with the implementation of the 3D attention module and feature pyramid network developed in this study to improve performance. For model evaluation, 130 videos were collected from YouTube including videos of four types of construction equipment at various construction sites. Trained on 520 clips and tested on 260 clips, ADVICE achieved precision and recall of 82.1% and 83.1%, respectively, with an inference speed of 36.6 frames per second. The evaluation results indicate that the proposed method can implement the 3D CNN‐based one‐stage model for real‐time action detection of construction equipment in videos of diverse, variable, and complex construction sites. The proposed method paved the way to improving safety, productivity, and environmental management of construction projects.

SNA-based multi-criteria evaluation of multiple construction equipment: A case study of loaders selection

Selecting the proper construction equipment is a challenging task owing to a wide range of available types as well as a host of criteria to be considered during decision making. To deal with this, a heterogeneous group decision-making framework to evaluate multiple purchasing choices of construction equipment is proposed with two data forms, i.e., 2-dimension uncertain linguistic variables (2DULVs) and real numbers. Firstly, a novel way to derive weights of experts by social network analysis (SNA) is applied considering trust degrees among experts in a social trust network. Secondly, after evaluation index system is established, 2DULVs that include both the linguistic evaluations on alternatives and decision makers’ appraisals on the given evaluation results are applied to represent subjective fuzzy evaluation information, while real numbers are used to represent quantitative values. Thirdly, 2-dimension uncertain linguistic power generalized weighted aggregation (2DULPGWA) operator is applied to aggregate evaluation values among experts. Fourthly, analytic hierarchy process (AHP) and entropy method are utilized to derive combined weights of sub-criteria before the rank can be obtained by the evaluation based on distance from average solution (EDAS) method. Finally, a case study to evaluate multiple choices of loaders is proposed and a comparative analysis is conducted to prove the effectiveness of the proposed framework. This study provides a meaningful guidance for the optimal selection among various types of construction equipment.

Пуассоновский процесс деградации машин: применение к стоимостной оценке

The machinery degradation process is described by a random process in which failures occur with constant intensity, and with each failure the rate of benefits generated by the machinery item reduces by a random amount. If the machinery item begins to generate negative benefits, it is subject to decommissioning. We get explicit expressions for the average life of the machinery items and the coefficient of variation of the service life. Machine’s value is determined by discounting the flow of benefits from its future use. This allows to link this value with the rate of benefits that the machinery item brings. In cases where there is no information on the amount of such benefits, appraisers rely on the machine’s age. However, different machinery items of the same age may be found in a different condition and therefore are characterized by different values. We offer formulas for calculating the percent good factors reflecting the average decrease in the equipment’s value with age. To take into account the effects of income tax, property tax and inflation, it suffices to adjust the discount rate in the constructed model. It has been verified that the proposed dependencies are in a fairly good agreement with market price data for two different types of construction equipment.

Full body pose estimation of construction equipment using computer vision and deep learning techniques

Construction sites are among the most hazardous places with various safety issues. The high rate of hazards on construction sites can be attributed to the dynamic and complex characteristics of construction-related entities, such as the movement of construction equipment and workers as well as the interactions among them. Tracking construction equipment and workers can help avoid potential collisions and other accidents to achieve safer on-site conditions. As construction equipment (e.g. excavators, trucks, cranes, and bulldozers) plays a significant role in construction projects, it is important to track the location, pose and movement of construction equipment. Currently, with the wide installation of surveillance cameras on construction sites, computer vision techniques are explored to process the captured surveillance videos and images, such as to monitor the site conditions and prevent potential hazards. Previous studies have attempted to identify and locate different types of construction equipment on construction sites based on surveillance videos using computer vision techniques. However, there are limited studies that automatically estimate the full body pose and movement of on-site construction equipment, which can greatly influence the safety condition of construction sites and the utilization of the equipment itself.In this study, a methodology framework is developed for automatically estimating the poses of different construction equipment in videos captured on construction sites using computer vision and deep learning techniques. Firstly, keypoints of equipment are defined, based on which the images collected from the surveillance cameras are annotated to generate the ground truth labels. 70%, 10%, and 20% of the annotated image dataset are used for training, validation and testing, respectively. Then, the architectures of three types of deep learning networks i.e. Stacked Hourglass Network (HG), Cascaded Pyramid Network (CPN), and an ensemble model (HG-CPN) integrating Stacked Hourglass and Cascaded Pyramid Network are constructed and trained in the same training environment. After training, the three models are evaluated on the testing dataset in terms of normalized errors (NE), percentage of correct keypoints (PCK), area under the curve (AUC), detection speed, and training time. The experiment results demonstrate the promising performance of our proposed methodology framework for automatically estimating different full body poses of construction equipment with high accuracy and fast speed. It is indicated by experiments that both HG and CPN can achieve relative high accuracy, with a PCK value of 91.19% and 91.78% respectively for estimating the equipment full body poses. In addition, the ensemble model with online data augmentation can further improve the accuracy, achieving a NE of 14.57 × 10−3, a PCK of 93.43%, and an AUC of 39.72 × 10−3 at the detection speed of 125 millisecond (ms) per image. This study lays the foundation for applying computer vision and deep learning techniques in the full body pose estimation of construction equipment, which can contribute to the real-time safety monitoring on construction sites.

Guidelines for Construction Vibrations: How Much Rattle is Too Much?

The study of construction/blast-related ground vibrations on structures, people, and objects is a surprisingly diverse field. Vibration sources vary from blasting, all types of construction equipment, tunneling, trains, trucks, and even pipe bursting. These vibrations can damage structures, annoy people, kill fish, and disturb sensitive equipment. Evaluating the effects of these vibrations is complicated by the existence of multiple guidelines, difficulty in documenting low-level damage, human response to vibrations, separating long-term environmental effects, geology, and wide variations in building design, construction, and their condition.

도로공사 공정계획을 위한 공정 로직 및 건설장비 효율화 방안

Highway construction is a combination of linear, repetitive, and highly equipment intensive operations. Various types of construction equipment are deployed to ensure undisrupted performance of construction, and thus productivity improvement and cost-saving can be achieved through well-thought-out planning. The selection of construction equipment is dependent upon construction sequence, site conditions, and construction methods. In the process of planning, management should consider various types of construction methods per each type of construction operation. Also, management should map out proper construction equipment operation plan that takes the construction duration and cost measures into consideration. However, limited availability of historic data from the similar types of operations has been a stumbling block to proper construction planning, making the operations performed based upon experience and intuition guided by rules-of-thumb. As a consequence, the planing phase rarely provided an adequate validity in the implementation phase. The researchers developed a process logic for each construction type that management can utilize from early phase of highway construction planning process. Moreover, derived the construction equipment combination optimized for efficiency by using the process simulation technique. The developed method is expected to be useful for the decision-making process that aims to evaluate efficiency of various process plans and to ensure optimal selection of construction equipment for highway construction projects.

The use of construction equipment productivity rate model for estimating fuel use and carbon dioxide (CO2) emissionsCase study: bulldozer, excavator and dump truck

Carbon dioxide (CO2) emission has drawn a great attention in recent years all over the world, and it plays a very important role in the process of global warming. The off-road equipment, which includes construction equipment, accounted for 7.5% emission of CO2. The objective of this article is to propose a groundwork for a methodology that can be used to estimate the total fuel use and CO2 emissions from construction equipment activities based on its productivity rate. Currently, there is not a methodology or technique for estimating productivity, fuel use and emissions at once. The multiple linear regression analysis has successfully modelled the productivity rate and good to be used as a benchmark for estimating fuel use and CO2 emissions from various types of construction equipment performing earthwork activities. The proposed methodology will help the contractor to estimate the total expected pollutant emissions for the project, which would be valuable information for a preliminary environmental assessment of the project. By using construction plans and specifications, the methodology and tool presented in this research can be used to estimate cost, fuel use and emissions from commercial, residential, industrial or heavy highway. By the proposed approach, it is possible to develop new fuel use and emissions inventories for construction industry in general.

Performance evaluation of excavator control device with EMG-based fatigue analysis

Among the many types of construction equipment, excavators have been widely used due to their versatility in various operations such as digging, crushing, and breaking, etc. In order to achieve more intuitive and intelligent control of the excavator, a new control device has recently been developed to replace the conventional joystick installed in excavators. This paper focuses on a performance evaluation method for this control device in order to determine its feasibility. The fatigue analysis method based on Electromyography (EMG) is utilized to evaluate the control performance of the devices with the assumption that fatigue can be the most critical criterion when determining an appropriate control device. Fatigue was determined by analyzing the EMG amplitude as well as the frequency spectrum. In order to investigate the feasibility of the proposed method, two different control devices are tested for performing leveling work: a haptic device and the conventional joystick lever. Through the experimental studies, it was found that the haptic device produced less muscular fatigue and the feasibility of the evaluation method was also verified.

Estimating the emissions of nitrogen oxides (NOX) and particulate matter (PM) from diesel construction equipment by using the productivity model

Purpose – This paper aims to present a synergistic approach that combines both construction and environmental expertise to lay the groundwork for a model that can be used to estimate the productivity rate and emissions from construction equipment activities.Design/methodology/approach – The proposed estimating tool is developed by combining the productivity rate model from a reliable construction estimating data sources and the calculation algorithm employed by the EPA's NONROAD model. In order to develop productivity models, simple earthwork activities involving bulldozer, excavator, and dump truck were selected.Findings – The MLR approach proved to be a useful alternative for estimating productivity rate of three pieces of equipment. The MLR models for the productivity rate can explain high percentage of the variability in the data. The models are good to be used as a benchmark for estimating NOX and PM emissions from some certain types of construction equipment performing earthwork activities. The prod...

Research and Design of Intelligent Building Energy Management System

In the field of intelligent building, managing energy consumption effectively in various types of construction equipment, improving energy efficiency, has been seen as an important and difficult subject. This thesis through establish building energy management system; realize building energy management by unified management of construction equipment.

건설기게의 대기오염물질 배출계수 산정을 위한 연구

Generally. mobile sources of air pollution were classified in on-road and non-road. Due to increased registration number of construction equipment in Korea. updated emission factors for non-road mobile sources, such as construction machinery. should be developed. NONROAD model of U.S. EPA already has introduced transient adjustment factors and sulfur adjustment factors for emission factors of diesel powered engine. In addition to this. European Environment Agency (EEA) has proposed emission factors for off-road machinery including several types of construction equipment. In this study. six types of construction equipment, such as excavator. forklift, loader, crane, roller and bulldozer, were studied to estimate emission factors based on total registration status in Korea. Total 445 construction equipments between 2004 and 2007 model year were tested with KC1-8 mode and air pollutants (CO, THC, <TEX>$NO_x$</TEX>, and PM) were measured. After statistical estimation and calculation, emission factors for CO, THC, <TEX>$NO_x$</TEX>, and PM for excavator, forklift, loader, crane, roller and bulldozer were provided and compared with previous emission factors. Moreover, updated emission factors for six types of construction equipment in this study were verified after comparison with emission factors of U.S. EPA. Finally, estimated emission amounts of four air pollutants were suggested according to six types of construction equipment.

Integrated wiring system for construction equipment

Advances in electronic technology have brought numerous changes in design of most types of construction equipment. Due to the electronic devices and electric loads, such as lamps and motors, a typical construction vehicle has many wires and connectors. The next phase of the changes is to focus on how to reduce these complex wires by using digital communications. This paper presents the development of an integrated wiring system for construction equipment. More specifically, an excavator that has more than 40 devices has been chosen in order to apply the concept of the integrated wiring system. After grouping electrical devices by their locations and functions, two communication controllers, i.e., an instrument controller and an engine-hydraulic controller, are defined. Two test controllers have been developed in order to prove the concept.

Prediction of Highway Construction Noise Levels

A model for the prediction of noise levels from highway construction operations has been developed under sponsorship of the Federal Highway Administration (FHWA). Noise sources are geometrically represented as points, lines and areas, and noise barrier attenuation may also be analyzed. Built into the model and accompanying computer program is a data base for over fifty types of construction equipment and operations. Use of the interactive computer program is illustrated through an example problem. While use of the model is not required, FHWA regulations do mandate that highway construction noise be addressed during the development of Federal‐aid highway projects. The model and program may therefore be used by highway noise control engineers for environmental impact assessment and for design of noise abatement measures.

DESIGN FOR ERECTION CONSIDERATIONS

For the erection of the hypothetical 24-story apartment building several types of construction equipment are discussed. Based on the selection of a climbing crane as a suitable method for erecting components of this building, a detailed analysis is performed on the crane capacities required, crane support frames, and typical floor girders of the structure to support the crane assembly. Additionally, foundation modifications are analyzed and discussed for the initial position of the climbing crane resting on ground level. Further discussions include climbing sequences, lateral restraint, and bracing.