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A heavy machinery operators' ability to adhere to schedules is crucial for the success of construction projects. However, unforeseen delays often occur during projects, forcing operators to expedite their work. This pressure often presents challenges for teleoperators. Completing tasks remotely typically takes longer than performing the same tasks on-site due to reduced situational awareness and reliance on technology for perception and understanding in remote workplaces. These inherent aspects of teleoperation add complexity to tasks, especially under time-constrained conditions. This study explores operators' cognitive and task performance during teleoperation of challenging tasks under various time pressures. Thirty-one participants operated a virtual excavator under four different levels of time pressure during the experiments. Results show that appropriate time pressure enhances task performance in aspects of safety, productivity, and quality, whereas excessive pressure results in cognitive overload, disengagement, impaired situational awareness, increased errors, and reduced productivity. This research contributes to enhancing the understanding of teleoperation from the operator's perspective, addressing cognitive challenges to improve safety and efficiency during the remote operation of heavy machinery in construction. [ABSTRACT FROM AUTHOR]

Accidents related to construction machinery primarily result from operators engaging in unsafe behavior. Continuous monitoring of the vital signs of heavy machinery operators is necessary to enable timely intervention in the event of abnormal physical or mental states, thereby reducing the risk of accidents. Researchers have proposed methods for monitoring vital signs in heavy machinery operators through wearable devices. However, sensor-based wearable systems, such as surface electromyography, are often invasive or necessitate the affixing of numerous sensors to the workers' bodies, which may result in discomfort for the workers and potential instability within the system, thereby constraining their utilization on construction sites. To mitigate these limitations, this study proposes a contactless monitoring method based on millimeter-wave (MMW) radar and tests the accuracy and robustness of the method. Ten participants of various figures were recruited for sensing and robustness tests within a simulated heavy machinery cabin. The experimental results demonstrate that MMW radar can accurately monitor the respiration rate and heart rate of heavy machinery operators within a range of ±45° horizontally and ±30° vertically and unaffected by illumination and attire conditions. The proposed method has great potential for enabling continuous monitoring of vital signs in heavy machinery operators in harsh construction environments. Further analysis of the vital sign information obtained from MMW radar can be involved to enable future research, such as fatigue monitoring and attention assessment. [ABSTRACT FROM AUTHOR]

This work presents an investigation of robotic technologies' effectiveness in construction activities. Sixty‐four highly relevant publications were identified from the database. By systematically reviewing the publications, the secondary data that are of interest to the review theme were retrieved and further evaluated. It is found that robotic technologies for automated construction is a growing field, where the taxonomy of robot was reflected in a diversified manner in the existing studies, ranging from the muscular guy—robotic manipulator—to the dexterous ones—unmanned aerial vehicle, autonomous mobile robot, automated guided vehicle, autonomous construction machinery and quadruped robot. In addition, the existing studies have provided substantial evidence to reveal the robotic technologies' effectiveness against traditional human methods in construction scenarios, and the measures for effectiveness consisted of productivity, precision, and success rate. With the evidence, it seems that the construction sector could benefit from robotic technologies to achieve intelligent workflows. Furthermore, based on the existing knowledge foundation in the current literature, a theoretical framework for future research direction is proposed. The framework envisages the integration of large models with construction robots to address operational inefficiencies, reduce costs, and simplify management. [ABSTRACT FROM AUTHOR]

Construction machinery plays a pivotal role in the industrial advancement of China, yet it also contributes to noise pollution that may adversely affect the auditory health of operators. Consequently, there is an increasing demand for enhancing the sound quality of construction machinery to adapt to complex environmental conditions and foster a more favorable acoustic environment. This study involved collecting sound samples from a specific loader model under various operating conditions and conducting a spectrum analysis to determine the amplitude frequency range. The analysis entailed comparing the characteristic loudness in objective terms with the frequency band range of noise sources to pinpoint the primary contributors to noise. Additionally, the study explored sound quality characteristics through the variations in loudness and sharpness, two objective metrics. The findings indicated that: (a) under the conditions of driving forward, reversing, and cab lifting, the peak characteristic loudness values were concentrated between 0.9-3.0 Bark and 0.2-1.8 Bark, respectively, with average loudness values for the left and right ears being 58.9 sone and 55.8 sone. The average sharpness values for the left and right ears were 2.57 acum and 2.48 acum, respectively; (b) the main noise sources within the driver's cab were identified as the cab panels and the engine's fundamental frequency, while external noise primarily emanated from the engine intake, exhaust, and the cooling fan; (c) loudness and sharpness values were higher during acceleration both forward and backward, and these values were also higher in the left ear than in the right during cab lifting. These conclusions provide a foundational and practical reference for future initiatives to enhance and optimize sound quality in high-end construction machinery. [ABSTRACT FROM AUTHOR]

Considering that there are many equipment and complex technologies in some remote regions, it is difficult to carry out equipment maintenance and support efficiently, due mainly to many disadvantages, e.g., inconvenient transportation, harsh natural environment conditions and etc. The characteristics of equipment maintenance and support mode in the remote region are analyzed, innovatively constructing a remote equipment maintenance support system architecture guided by state based maintenance. An autonomous maintenance and support platform for equipment in the remote region with fault prediction and health management as the core is designed, which optimize the maintenance and support process for equipment in remote areas, improve timeliness and completeness. It provides an applicable and sustainable full life cycle guarantee model for the construction and upgrading of equipment maintenance and support system in remote areas. [ABSTRACT FROM AUTHOR]

Thailand's aerial geostrategic importance led the United States to offer a generous air aid programme in the 1950s. The US funded the refurbishment of airfields across the country: the extension and hardening of airstrips, and the construction of new tarmacs, terminal buildings and navigation equipment. They also provided technical training to Thai nationals. The leading US international airline Pan American Airways helped modernize Thai Airways. In this paper, I explore the broad outlines of this programme and focus in particular on Pan American's assistance. I argue that although the airport and navigation assistance portions of the programme were largely successful, the Pan American assistance side of the project largely failed to meet expectations. [ABSTRACT FROM AUTHOR]

To improve emissions inventory, design a real-work operation mode that simulates the operating characteristics of an excavator. The test was conducted at a specialized construction machinery test site to maintain constant operator and soil conditions. Engine speed and actual engine percentage torque were obtained from the onboard diagnostic terminal through a data acquisition device. In Korea, a fixed LF of 0.48 is uniformly applied to all construction machinery. However, it may not be entirely reasonable to use this LF for construction machines performing a variety of tasks. In practical operation tests conducted on two excavators, the LF was measured as 0.426 and 0.47, demonstrating that the fixed LF may not always be applicable. By implementing an LF that is subdivided for each specific type of construction machine, the error in emission calculations could potentially be reduced by 2% to 12%. [ABSTRACT FROM AUTHOR]

Layered continuous paving (LCP) technology is an effective choice for improving the integrity and construction efficiency of the thick cement-stabilised macadam (CSM) base course. However, the effects of re-vibration generated by construction equipment during the paving process are not well understood. In this investigation, the effects of re-vibration on mechanical properties and air-void characteristics were investigated by mechanical performance tests, chloride migration experiments and industrial computerised tomography tests. The results demonstrate that compared with the control group, the void characteristic parameters were reduced by applying re-vibration within 12 h, resulting in a more compact structure. Thus, the mechanical properties were improved. However, the improvement effect gradually weakened over the delay time. CSM interlayer bonding strength were related to the time point of applying re-vibration and the cement paste dosage sprinkled between the layers. It is suggested that12 h be used as the critical time point for the LCP. [ABSTRACT FROM AUTHOR]

Internal curve motors (ICMs) are used in construction and port machinery owing to their low speed and strong torque. The internal leakage of an ICM has a direct impact on its working performance; however, research on the internal leakage of ICMs is unclear. A method, based on the Computational Fluid Dynamics (CFD) transient simulation of an ICM, for obtaining the transient pressure in the plunger chamber and combining the mathematical model of internal leakage is calculated, and the internal leakage is proposed. We one-factor analyzed the internal leakage of the ICM and the effect of the valve pair and plunger pair leakage, and conducted multifactor analysis on the effect of the interaction between those two factors on the internal leakage of the ICM. The results show that the internal leakage mechanisms affecting the ICM were, in descending order of impact, the inlet pressure, oil temperature, and rotational speed, and a significant interaction existed between the inlet pressure and oil temperature, whereas the influence of rotational speed was not significant. [ABSTRACT FROM AUTHOR]

Copyright of Internationales Handelsrecht is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The conventional hydraulic system of excavators suffers from significant valve throttling losses and inadequate matching between the hydraulic power source and the load, which substantially impact the system's overall energy consumption and severely impede the trend toward electrification and energy efficiency in construction machinery. To address this issue, a pump-controlled hydraulic cylinder system has been implemented to replace the original valve-controlled hydraulic system that utilizes a single pump with multiple actuators. The influence of energy conversion efficiency and the speed between the motor and the hydraulic pump under varying load-power conditions has been determined through experimental investigations. Based on these findings, a compound-control strategy is proposed that adjusts the displacement of the hydraulic pump to achieve precise control over the position of the hydraulic cylinder and facilitates both the speed and displacement coordination while ensuring optimal motor speed matching with the load power. This strategy is implemented in the boom pump's hydraulic cylinder control system. The research findings indicate that this combined-control approach enhances efficiency by approximately 18.9% compared with traditional variable-speed pump-controlled hydraulic cylinder systems. Furthermore, energy consumption is reduced by about 39% compared with the conventional valve-controlled hydraulic system. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the growing need for precise campus electricity management, understanding load patterns is crucial for improving energy efficiency and optimizing energy use. However, detailed electricity load data for campus buildings and their internal equipment is often lacking, hindering research. This paper introduces an energy consumption monitoring dataset from The Hong Kong University of Science and Technology (HKUST) campus in Hong Kong, comprising data from over 1400 meters across more than 20 buildings and collected over two and a half years. Using the Brick Schema curation strategy, raw data was curated into a research-ready format. This dataset supports various research tasks, including load pattern recognition, fault detection, demand response strategies, and load forecasting. [ABSTRACT FROM AUTHOR]

• Smoke and NO x exhaust emissions from a total of 905 construction machines, including 4 different types. • Substantial field measurements were carried out on construction sites in Beijing, giving an indication of real-world emissions. • Measured smoke result indicate that the implementation of strict regulatory policies is contribute to reducing emission. • Measured NO x emissions indicate that stricter emission standard stage cannot control the NO x level. To understand the smoke level and NO x emission characteristics of in-use construction machinery in Beijing, we selected 905 construction machines in Beijing from August 2022 to April 2023 to monitor the emission level of smoke and NO x. The exhaust smoke level and excessive emission situation of different machinery types were identified, and their NO x emission levels were monitored according to the free acceleration method. We investigated the correlation of NO x and smoke emission, and proposed suggestions for controlling pollution discharge from construction machinery in the future. The results show that the exhaust smoke level was 0–2.62 m−1, followed a log-normal distribution (μ = -1.73, δ = 1.09, R 2 = 0.99), with a 5.64% exceedance rate. Differences were observed among machinery types, with low-power engine forklifts showing higher smoke levels. The NO x emission range was 71–1516 ppm, followed a normal distribution (μ = 565.54, δ = 309.51, R 2 = 0.83). Differences among machinery types were relatively small. Engine rated net power had the most significant impact on NO x emissions. Thus, NO x emissions from construction machinery need further attention. Furthermore, we found a weak negative correlation (p < 0.05) between the emission level of smoke and NO x , that is the synergic emission reduction effect is poor, emphasizing the need for NO x emission limits. In the future, the oversight in Beijing should prioritize phasing out China Ⅰ and China Ⅱ machinery, and monitor emissions from high-power engine China Ⅲ machinery. [Display omitted] [ABSTRACT FROM AUTHOR]

In recent years, Japan has experienced numerous natural disasters, such as typhoons and earthquakes. Teleoperated ground robots (including construction equipment) are effective tools for restoration work at disaster sites and other locations that are dangerous and inaccessible to humans. Using visual information obtained from various viewpoints by a drone can allow for more effective remote control of a teleoperated ground robot, making it easier for the robot to perform a task. We previously proposed and developed a remote-controlled drone system using only human eyes. However, the effectiveness of using this drone system during the remote control of a robot has never been verified. In this paper, as the first step in verifying the effectiveness of the remote-controlled drone system using only the eyes when remote-controlling a robot, we consider its effectiveness in a simple task based on the task times, subjects' eye fatigue, and subjective evaluations of subjects. First, the previously proposed drone system is briefly described. Next, we describe an experiment in which a drone was controlled by the eyes using the drone system while a robot was controlled by hand, and an experiment in which both the drone and robot were controlled by hand without using the drone system. Based on the experimental results, we evaluate the effectiveness of the remote-controlled drone system using only the eyes when remote-controlling a robot. [ABSTRACT FROM AUTHOR]

In view of the problems of low detection accuracy, long detection time, and inability to monitor fault data in real time in the fault detection of traditional machinery and equipment, this paper studies the identification and fault detection of industrial machinery based on the Internet of Things (IoT) technology. By using Internet of Things technology to build a mechanical equipment fault detection system, Internet of Things technology can better build diagnostic and early warning modules for the system, so as to achieve the goal of improving the accuracy of equipment fault detection, shortening equipment fault detection time, and remotely monitoring equipment. The fault detection system studied in this paper has an accuracy rate of more than 93.4% to detect different types of fault. The use of Internet of Things technology is conducive to improving the accuracy of mechanical equipment fault detection and realizing real-time monitoring of equipment data. [ABSTRACT FROM AUTHOR]

Copyright of China Rubber Industry is the property of Editorial Office of China Rubber Industry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mechanized construction is being fully implemented in the electric power infrastructure domain to ensure construction safety, enhance project quality, and improve efficiency. Traditional methods of designing mechanized construction plans are often inefficient due to their labor-intensive processes and heavy reliance on human expertise. This study introduces and evaluates an ontology-guided system designed to automate mechanized construction planning for power grid projects. The developed ontology effectively models domain-specific knowledge, enabling the semantic integration of data from various sources. By leveraging SPARQL queries, the ontology-guided system incorporates knowledge reasoning capabilities that facilitate the automated selection of construction equipment and the generation of comprehensive construction plans. A prototype system incorporating an ontology-guided mechanism has been developed, showcasing marked enhancements in efficiency and accuracy over traditional manual methods, as evidenced by case studies and expert evaluations. The research results emphasize the potential of ontology-guided systems in innovating architectural planning, providing an extensible and standardized approach. Expert evaluation indicates that the system achieves 71.38% effectiveness in generating mechanized construction plans. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the case of technological construction equipment used for earth excavation, such as the excavator, productivity is dependent on the cycle time. The working cycle time is dependent on the movements of the arm/handle assembly in the digging process. The older generation machines are not equipped with systems for monitoring the movements of the arm-handle assembly and for this situation we have developed the solution presented in this paper. The research presents a solution to analyse the excavator arm trajectory using a drone and a specialized software used in trajectory analysis. The analysis of the movements of the arm-handler assembly allows the choice of optimal digging trajectories. The optimization of the trajectory in the earth excavation process increases the excavator's productivity and contributes to the reduction of fuel consumption with a positive effect on the reduction of greenhouse gases in the excavation process. [ABSTRACT FROM AUTHOR]

To address the frequent external damage incidents to transmission line corridors caused by construction machinery such as excavators and cranes, this paper constructs a dataset of external damage hazards in transmission line corridors and proposes a detection method based on YOLO-LSDW for these hazards. Firstly, by incorporating the concept of large separable kernel attention (LSKA), the spatial pyramid pooling layer is improved to enhance the information exchange between different feature levels, effectively reducing background interference on external damage hazard targets. Secondly, in the neck network, the traditional convolution is replaced with a ghost-shuffle convolution (GSConv) method, introducing a lightweight slim-neck feature fusion structure. This improves the extraction capability for small object features by fusing deep semantic information with shallow detail features, while also reducing the model's computational load and parameter count. Then, the original YOLOv8 head is replaced with a dynamic head, which combines scale, spatial, and task attention mechanisms to enhance the model's detection performance. Finally, the wise intersection over union (WIoU) loss function is adopted to optimize the model's convergence speed and detection performance. Evaluated on the self-constructed dataset of external damage hazards in transmission line corridors, the improved algorithm shows significant improvements in key metrics, with mAP@0.5 and mAP@0.5:0.95 increasing by 3.4% and 4.6%, respectively, compared to YOLOv8s. Additionally, the model's computational load and parameter count are reduced, and it maintains a high detection speed of 96.2 frames per second, meeting real-time detection requirements. [ABSTRACT FROM AUTHOR]

Applying deep learning algorithms in the construction industry holds tremendous potential for enhancing site management, safety, and efficiency. The development of such algorithms necessitates a comprehensive and diverse image dataset. This study introduces the Aerial Image Dataset for Construction (AIDCON), a novel aerial image collection containing 9563 construction machines across nine categories annotated at the pixel level, carrying critical value for researchers and professionals seeking to develop and refine object detection and segmentation algorithms across various construction projects. The study highlights the benefits of utilizing UAV-captured images by evaluating the performance of five cutting-edge deep learning algorithms—Mask R-CNN, Cascade Mask R-CNN, Mask Scoring R-CNN, Hybrid Task Cascade, and Pointrend—on the AIDCON dataset. It underscores the significance of clustering strategies for generating reliable and robust outcomes. The AIDCON dataset's unique aerial perspective aids in reducing occlusions and provides comprehensive site overviews, facilitating better object positioning and segmentation. The findings presented in this paper have far-reaching implications for the construction industry, as they enhance construction site efficiency while setting the stage for future advancements in construction site monitoring and management utilizing remote sensing technologies. [ABSTRACT FROM AUTHOR]

Construction equipment operators (CEOs), who are required to work in seated positions for prolonged periods, often develop excessive mental fatigue, causing human error-related accidents, lower productivity, and psychological illnesses. However, the current practice for assessing fatigue is limited on construction sites. Previous studies utilizing smartwatches, electroencephalography, or eye-tracking technologies are intrusive and not convenient since they require operators to wear special devices, while vision-based solutions are sensitive to lighting conditions and have serious privacy concerns. There is a demand for continuously and accurately monitoring CEOs' mental fatigue levels without causing discomfort and aversion. This study introduces a noninvasive and noncontact smart cushion method to bridge the knowledge gap. We first developed a smart cushion system incorporating optical fiber sensors to collect human heartbeat and respiration data. Then, we adopted the Bidirectional Long-Short-Term Memory (BiLSTM) model to recognize fatigue states. An experiment was conducted in which data was collected from 16 subjects engaged in simulated excavation tasks. Experimental results demonstrate the feasibility of the proposed method, and the BiLSTM model obtained an accuracy of 94.0%. The proposed smart cushion method could also be convenient for understanding ergonomic risks resulting from prolonged sitting, a grave occupational health and safety problem that plagues various industries. Practical Applications: This study presents a smart cushion–based framework to continuously monitor the mental fatigue states of construction equipment operators (CEOs) during daily work. The proposed solution has clear advantages: it (1) is nonintrusive since it no longer requires sensors attached to the skin of operators, (2) is not sensitive to dynamic lighting conditions and does not generate privacy concerns, (3) is easy-to-use since it can be placed on the operator's seat or seatback and does not need additional power. Experimental results also demonstrated that the proposed Gaussian mixture model and Bidirectional Long-Short-Term Memory model can achieve effective data processing and accurate fatigue recognition. The proposed system can provide construction managers with a quantitative and reliable assessment tool to measure CEOs' mental workloads and support the intervention (e.g., worker shifts and breaks). If wirelessly connected to a smartphone, the smart cushion can conveniently provide early warnings to operators and their managers to alert the operators at stake to take breaks/rests to avoid mental fatigue-related ill consequences. Moreover, the proposed solution could be used to study and prevent the risks resulting from prolonged sitting, which is a grave occupational health and safety problem plaguing many industries. [ABSTRACT FROM AUTHOR]

Copyright of Automotive Digest is the property of Automotive Digest Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Truck cranes, which are crucial construction equipment, need to maintain good operational performance to ensure safe use. However, the complex and ever-changing working conditions they face often make it challenging to test their performance effectively. To address this issue, a multi-input and multi-output soft sensor technology model is suggested, utilizing a graph convolutional network and random forest to predict key performance indicators of crane operations such as luffing, telescoping, winching, and slewing under varying conditions. This method aims to streamline the process of testing and debugging truck cranes, ultimately reducing time and costs. Initially, the graph convolutional network model is employed to extract relevant feature information linked to the target variable. Subsequently, using this feature information and the RF model, multiple decision trees are constructed for regression prediction of the target variables. An operational dataset reflecting the crane's actual working conditions is then generated to assess the graph convolutional network and random forest model. The effectiveness of this approach is further confirmed through comparisons with other methods like gradient boosting trees, support vector regression, and multi-layer perceptron. [ABSTRACT FROM AUTHOR]

The multiway valve is the core control element of the hydraulic system in construction machinery, such as excavators. Its complex internal structure, especially the flow channels, significantly impacts the machine's efficiency and reliability. This study focuses on the boom flow channel of excavator multiway valves and establishes a multi-physical field coupling simulation model. We propose six key flow channel structural parameters and analyze changes in the valve's flow field, temperature field, and structural field using orthogonal test simulation data. The range analysis method identifies the primary and secondary influences of structural parameters on pressure loss, temperature, stress, and strain. A multi-objective optimization model was developed using a neural network and the Non-dominated Sorting Genetic Algorithm II(NSGA-II), with pressure loss and maximum stress as the optimization objectives. The Pareto front solution set for key flow channel parameters was calculated. The optimization results showed a 9.0% reduction in pressure loss and a 40.7% reduction in maximum stress. A test bench verified the simulation model, achieving prediction accuracies of 94.8% for pressure loss in the inlet area and 92.3% in the return area. This method can provide a reference for the optimal design of the dynamic characteristics of high-pressure multiway valves. [ABSTRACT FROM AUTHOR]

Excavating super-large-span tunnels in soft rock masses presents significant challenges. To ensure safety, the sequential excavation method is commonly adopted. It utilizes internal temporary supports to spatially partition the tunnel face and divide the excavation into multiple stages. However, these internal supports generally impose spatial constraints, limiting the use of large-scale excavation equipment and reducing construction efficiency. To address this constraint, this study adopts the "Shed-frame" principle to explore the feasibility of an innovative support system, which aims to replace internal supports with prestressed anchor cables and thus provide a more spacious working space with fewer internal obstructions. To evaluate its effectiveness, a field case involving the excavation of a 24-m span tunnel in soft rock is presented, and an analysis of extensive field data is conducted to study the deformation characteristics of the surrounding rock and the mechanical behavior of the support system. The results revealed that prestressed anchor cables integrated the initial support with the shed, creating an effective "shed-frame" system, which successively maintained tunnel deformation and frame stress levels within safe regulatory bounds. Moreover, the prestressed anchor cables bolstered the surrounding rock effectively and reduced the excavation-induced disturbance zone significantly. In summary, the proposed support system balances construction efficiency and safety. These field experiences may offer valuable insights into the popularization and further development of prestressed anchor cable support systems. [ABSTRACT FROM AUTHOR]

"Demand traction, technology promotion" is the main rule of the development of equipment system construction. This paper focuses on the analysis of system requirements, and puts forward the process of system requirements analysis on the basis of analyzing the basic connotation of system requirements analysis, combining the advantages of threat and capability; aiming at the key technical problems such as capability gap measurement and mapping analysis in system requirements analysis. The capacity gap measurement model is established, and the QFD method is used to realize the mapping between the capability requirement-system requirement-technology requirement, and the analysis of the typical space target monitoring system is taken as an example to verify the system demand analysis method proposed in this paper, which provides a theoretical basis for carrying out the system demand analysis. [ABSTRACT FROM AUTHOR]

Tunnel boring machines (TBMs) are used to excavate tunnels in a manner where the rock is constantly penetrated with rotating cutter heads. Fine particles of the rock minerals are thereby generated. Workers on and in the vicinity of the TBM are exposed to particulate matter (PM) consisting of bedrock minerals including α-quartz. Exposure to respirable α-quartz remains a concern because of the respiratory diseases associated with this exposure. The particle size distribution of PM and α-quartz is of special importance because of its influence on adverse health effects, monitoring and control strategies as well as accurate quantification of α-quartz concentrations. The major aim of our study was therefore to investigate the particle size distribution of airborne PM and α-quartz generated during tunnel excavation using TBMs in an area dominated by gneiss, a metamorphic type of rock. Sioutas cascade impactors were used to collect personal samples on 3 separate days. The impactor fractionates the dust in 5 size fractions, from 10 µm down to below 0.25 µm. The filters were weighted, and the α-quartz concentrations were quantified using X-ray diffraction (XRD) analysis and the NIOSH 7500 method on the 5 size fractions. Other minerals were determined using Rietveld refinement XRD analysis. The size and elemental composition of individual particles were investigated by scanning electron microscopy. The majority of PM mass was collected on the first 3 stages (aerodynamic diameter = 10 to 0.5 µm) of the Sioutas cascade impactor. No observable differences were found for the size distribution of the collected PM and α-quartz for the 3 sampling days nor the various work tasks. However, the α-quartz proportion varied for the 3 sampling days demonstrating a dependence on geology. The collected α-quartz consisted of more particles with sizes below 1 µm than the calibration material, which most likely affected the accuracy of the measured respirable α-quartz concentrations. This potential systematic error is important to keep in mind when analyzing α-quartz from occupational samples. Knowledge of the particle size distribution is also important for control measures, which should target particle sizes that efficiently capture the respirable α-quartz concentration. [ABSTRACT FROM AUTHOR]

Previous studies have investigated the health hazards caused by exposure to the noise of heavy equipment used at construction sites. Korea's Ministry of Land, Infrastructure, and Transport operates a registration system for construction machinery, and regular safety inspections are mandatory. Although workers inspecting construction machinery vehicles are exposed to unwanted noise, no noise exposure assessment has been made regarding these workers in Korea. The time-weighted average (TWA) daily average noise exposure level among construction machinery inspectors at 18 inspection centers was 75.3 dB(A). Among the inspection steps, the exhaust gas inspection step was found to exhibit the highest noise exposure level, up to 100 dB(A). In Korea, if the noise level of a workplace exceeds 85 dB(A) as a TWA, workers are required to undergo special medical examinations. This study found that special medical examinations were required for two of the 18 target inspection centers (approximately 115 workers) as the 8-hour TWA noise level exceeded 85 dB(A). Therefore, regular noise exposure assessment and special medical examinations for noise are required to prevent inspectors from developing hearing disorders due to noise exposure. [ABSTRACT FROM AUTHOR]

This study examines the habits and attitudes of construction sector employees in Türkiye regarding the use of personal protective equipment (PPE). A comprehensive face-to-face survey was conducted with 398 randomly selected employees from small- and medium-sized construction companies across various regions of Türkiye. The survey aimed to identify the frequency of PPE usage and the factors influencing its use and to provide actionable suggestions for improving PPE compliance. The results indicate that while a majority of workers recognize the importance of PPE, actual usage rates are inconsistent. Key factors affecting PPE use include the ergonomics and comfort of the equipment as well as worker perceptions and attitudes. Notably, the study found that the discomfort associated with PPE and the belief that it slows down work pace are significant barriers to its regular use. Additionally, the research highlights the need for a robust punishment–reward system to enforce PPE compliance and suggests that more stringent legislative measures are required to enhance occupational health and safety (OHS) standards in the construction industry. This study contributes to the existing body of knowledge by providing a detailed analysis of PPE usage in the Turkish construction sector and offering practical recommendations for policymakers and industry stakeholders. Implementing these suggestions can lead to improved worker safety and reduced occupational accidents. [ABSTRACT FROM AUTHOR]

Introduction. For high-rise building construction self-climbing platforms based on hydraulic drive are used, lifting the formwork for monolithic structures. These lifting mechanisms differ in structures, characteristics and parameters. There is a need to introduce high-tech equipment in the industry. To create self-climbing platforms for high-rise construction, it is necessary to conduct research in the field of operation of lifting vehicles.Materials and methods. Platforms based on climbing profile for the analysis were used. The design features of the specified type of platforms were considered. The main parameters were speed of lifting, maximum lifting height and load capacity.Results. Graphical data on the main structural elements and the lifting process were obtained the specified type of platforms. Graphical data on hydraulic system is provided and the mechanism of movement is considered in detail. The basic formulas for determining the platform lifting speed and maximum load capacity required for the choosing of specified lifting vehicles.Discussion and conclusion. Existence of factors affecting the lifting speed of platforms and their maximum load capacity provides grounds for the development of a method for calculating and designing the considered transport and technological equipment in Russian Federation. One of the ways to develop this lifting equipment is integration of accessory mechanisms into their structure for fulfilling additional works during the construction of high-rise buildings, in this way forming a single transport and technological complex.