Earthwork is seemingly guesswork, but it requires a high level of accuracy and precise planning. Differences between earthwork design and finishing levels cause project delays and cost overrun due to the time-consuming nature of earthwork re-work. Therefore, error-free earthwork planning and design review is a key to the success of earthwork projects. This study utilized an integrated approach of an unmanned aerial vehicle (UAV)-based point cloud and BIM (Building Information Modeling) to verify the design and to operate the earthwork planning. The integrated approach was proposed and applied to a 420 square meters housing construction project to review an original earthwork design and create an earthwork plan for excavator work. As a result, errors in earthwork design that caused by inaccurate initial DEM was revealed, thus the earthwork design was revised with a UAV-based point cloud map. Additionally, the integrated approach was able to generate an explicit task sequence for an excavator.

Advancements in deep learning and vision-based activity recognition development have significantly improved the safety, continuous monitoring, productivity, and cost of the earthwork site. The construction industry has adopted the CNN and RNN models to classify the different activities of construction equipment and automate the construction operations. However, the currently available methods in the industry classify the activities based on the visual information of current frames. To date, the adjacent visual information of current frames has not been simultaneously examined to recognize the activity in the construction industry. This paper proposes a novel methodology to classify the activities of the excavator by processing the visual information of video frames adjacent to the current frame. This paper follows the CNN-BiLSTM standard deep learning pipeline for excavator activity recognition. First, the pre-trained CNN model extracted the sequential pattern of visual features from the video frames. Then BiLSTM classified the different activities of the excavator by analyzing the output of the pre-trained convolutional neural network. The forward and backward LSTM layers stacked on help the algorithm compute the output by considering previous and upcoming frames’ visual information. Experimental results have shown the average precision and recall to be 87.5% and 88.52%, respectively.

With the increasing need for particulate matter (PM) monitoring, the demand for light-scattering sensors that allow for real-time measurements of PM is increasing. This light-scattering method involves irradiating light to the aerosols in the atmosphere to analyze the scattered light and measure mass concentrations. Humidity affects the measurement results. The humidity in an outdoor environment may exist as gas or steam, such as fog. While the impact of humidity on the light-scattering measurement remains unclear, an accurate estimation of ambient PM concentration is a practical challenge. Therefore, this study investigated the effects of humidity on light-scattering measurements by analyzing the variation in the PM concentration measured by the sensor when relative humidity was due to gaseous and steam vapor. The gaseous humidity did not cause errors in the PM measurements via the light-scattering method. In contrast, steam humidity, such as that caused by fog, resulted in errors in the PM measurement. The results help determine the factors to be considered before applying a light-scattering sensor in an outdoor environment. Based on these factors, directions for technological development can be presented regarding the correction of measurement errors induced by vapor in outdoor environments.

The impacts of concrete on global warming through its use in structures such as buildings and infrastructure must be identified and better understood, as concrete is known to have a very high global warming potential (GWP). However, in contrast with ordinary on-site constructed reinforced concrete, GWPs of off-site factory-made prefabricated concrete products such as precast concrete (PC) and concrete piles that are widely used in construction are rarely evaluated, owing to the complicated manufacturing processes that make the determination of greenhouse gas emission difficult. In this study, the embodied life cycle GWPs were derived for PC and pretensioned spun high-strength concrete (PHC) piles to enable precise assessment of the global warming impact of concrete structures and the concrete industry of Korea. The determined embodied GWPs of PC and PHC piles were 1.77 × 10⁻¹ kg CO₂ eq/kg and 1.87 × 10⁻¹ kg CO₂ eq/kg, respectively. As a result, both prefabricated concrete products were determined to have high GWP due to input materials, such as cement rebars, while the GWP contributions of the off-site prefabrication processes were low. Moreover, the embodied GWPs of both prefabricated concrete products were significantly higher than those of ordinary reinforced concrete, and the impact of both products on global warming was found to be approximately 4% of the impact of the Korean concrete industry. This indicates that it is necessary to consider the impacts of the PHC pile and PC industries when assessing the impacts of greenhouse gas occurring in the concrete industry at the national level. It is expected that these findings will be widely used to obtain a more accurate assessment of the impact of concrete structures and industry on global warming.

Excavator–truck operations, characterized by their repetitive excavation and loading tasks, present a prime candidate for automation. While numerous studies have aimed to automate the earthworks, practical implementations remain scarce. This research introduces a task component design focused on excavator–truck operation planning to improve the functionality of an earthwork automation system. To address this, fundamental task primitives necessary for executing excavation tasks were engineered, and a web-based system was developed to automate the generation of work plans for both point and trench excavation through algorithmic processes. Additionally, a JSON-based protocol was introduced to facilitate efficient integration with other subsystems. Field tests were conducted to validate the effectiveness of the newly developed algorithm and protocol within the broader context of earthwork automation systems. The results demonstrated the successful implementation of the task components, confirming their operational viability and seamless integration into the existing earthwork automation framework.

This study addresses occupational safety in reinforced concrete construction, an area marked by high accident rates and significant worker injury risks. By focusing on activity–body part (A–BP) combinations, this research introduces a novel framework for quantifying injury risks across construction activities. Reinforced concrete construction tasks are categorized into ten specific activities within three major work types: rebar work, formwork, and concrete placement. These are further analyzed concerning six critical body parts frequently injured on-site: head/face, arm/shoulder, wrist/hand, torso, leg/pelvis, and foot/ankle. Using data from 2283 construction accident reports and expert surveys, the probability and severity of injuries for each A–BP element were calculated. Probability scores were derived from actual incident data, while severity scores were determined via expert evaluations, considering injury impact and the required recovery time. To ensure precision and comparability, scores were standardized across scales, enabling a final risk assessment for each A–BP. Results identified that wrist and hand injuries during rebar work activities, particularly cutting and shaping, exhibited the highest risk, underscoring the need for focused protective measures. This study contributes to construction safety management by providing detailed insights into injury risk based on activity–body part interactions, offering safety managers data-driven recommendations for tailored protective equipment, enhanced training, and preventive protocols. This research framework not only helps optimize safety interventions on conventional construction sites but also establishes a basis for future studies aimed at adapting these strategies to evolving construction methods.