Search Results (102)

To address the “silo effect” in construction waste management and the inefficiency of resource allocation in large-scale, multi-section engineering projects, this study developed a cloud-based regional collaborative platform for construction waste management. The platform adopts a technical framework based on Java 1.8.0, Spring Boot 2.4.4, and MySQL 8.0.16, and integrates a visual interactive interface. It supports dynamic access, data entry, quality review, and scheduling of construction waste information across multiple sections and projects. Validated through a case study on the Changhu section of the Guangdong Guanshen–Changhu Expressway expansion project, the platform successfully achieved spatial–temporal optimization of 740 thousand cubic meters of diversified construction waste across seven sections. The comprehensive utilization rate of construction waste increased by more than 25%. Practice has shown that the platform effectively promotes carbon emission reduction in earthworks, enhances resource circularity, and provides digital support for construction quality control. This platform presents an innovative informatics-driven approach to construction waste management, serving as a replicable model. Full article

Earthwork operations constitute a substantial share of infrastructure project costs and are critical to overall project efficiency. However, the construction industry still relies on conventional approaches and there is a lack of integrated fleet management systems for collaboratively working equipment. While telematics is widely used in other industries, its applications to monitor the complex interactions between excavators, dump trucks, and dozers in real time remain limited. This study proposes an intelligent fleet monitoring system that utilizes only satellite navigation data (GNSS) to analyze the real-time productivity of multiple earthwork machines without relying on additional sensors, such as IMU or accelerometers, thereby eliminating the need for separate measurement procedures. A lightweight site configuration step is required to define the work area/loading/dumping geofences on an existing site map. This research provides novel developed algorithms that facilitate a real-time productivity assessment for several earthwork equipment and provide planning-level recommendations for equipment deployment combinations. Dedicated motion classification algorithms were developed for excavators, dump trucks, and dozers to distinguish activity states, to compute working and idle times, and to quantify operational efficiency. The system integrates a web-based e-Fleet Management platform and a mobile e-Map application for visualization and equipment optimization. Field validation was conducted on two active earthwork projects to evaluate accuracy and feasibility. The results demonstrate that the developed algorithms achieved classification and productivity estimation errors within 2.5%, while enabling optimized equipment combinations and improved cycle time efficiency. The proposed system offers a practical, sensor-independent approach for enhancing productivity monitoring, real-time decision-making, and cost efficiency in large-scale earthwork operations. Full article

Earthwork volume calculation is a fundamental process in civil engineering and construction, requiring high-precision terrain data to assess ground stability encompassing load-bearing capacity, susceptibility to settlement, and slope stability and to ensure accurate cost estimation. However, seasonal and environmental constraints pose significant challenges to surveying. This study employed unmanned aerial vehicle (UAV) photogrammetry and light detection and ranging (LiDAR) mapping to evaluate the accuracy of digital terrain model (DTM) generation and earthwork volume estimation in densely vegetated areas. For ground extraction, color-based indices (excess green minus red (ExGR), visible atmospherically resistant index (VARI), green-red vegetation index (GRVI)), a geometry-based algorithm (Lasground (new)) and their combinations were compared and analyzed. The results indicated that combining a color index with Lasground (new) outperformed the use of single techniques in both photogrammetric and LiDAR-based surveying. Specifically, the ExGR–Lasground (new) combination produced the most accurate DTM and achieved the highest precision in earthwork volume estimation. The LiDAR-based results exhibited an error of only 0.3% compared with the reference value, while the photogrammetric results also showed only a slight deviation, suggesting their potential as a practical alternative even under dense summer vegetation. Therefore, although prioritizing LiDAR in practice is advisable, this study demonstrates that UAV photogrammetry can serve as an efficient supplementary tool when cost or operational constraints are present. Full article

Over the past few years, various research has been conducted to utilize 3D point cloud data in construction sites. This is because 3D point cloud data contain a variety of information, such as spatial coordinates (X, Y, Z), intensity, and color (RGB), making them highly applicable to construction environments that require precise operations. Accordingly, this research developed a new terrain surface interpolation method that leverages diverse information embedded in large-scale 3D point cloud data acquired from earthwork sites, as part of a foundational study for construction automation. In particular, the proposed terrain surface interpolation method was designed to be integrated with semantic segmentation based on 3D point cloud data, with a focus on enhancing the accuracy of earthwork volume estimation. Furthermore, field experiments were conducted using heavy construction equipment to compare terrain change and earthwork volume analyses between 3D point cloud data with and without the application of the proposed interpolation method. The analysis results of earthwork volumes indicated that the application of the terrain interpolation method to 3D point cloud data for construction equipment reduced estimation errors by approximately 94% compared to non-interpolated data. These findings demonstrate the effectiveness of the proposed method and are expected to contribute to future research in artificial intelligence and robotics utilizing 3D point cloud data within the construction industry. Full article

In current practice, the deployment of artificial intelligence models for the optimization of construction processes is highly complex and limited, primarily due to the lack of data available for training models. Collecting real-world data is both time-consuming and resource-intensive. This paper focuses on the development of a methodology and a model for generating synthetic data intended for the subsequent training of artificial intelligence models for optimizing construction machinery assemblies. The proposed synthetic data generation process is based on simulation principles that employ queuing theory and the stochastic Monte Carlo method. This approach enables the rapid creation of large-scale synthetic datasets. The developed model and generator are specifically focused on the use of construction machinery in earthworks. Selected generated data were compared with and validated against real construction projects. The synthetic data demonstrated very good agreement with the observed data across key performance indicators. For Total Cost, CO₂ Emissions, Fuel Consumption, and Completion Time, deviations between synthetic and real project data were generally within 5–7%, which is considered acceptable for construction process simulations. In contrast, the Number of Failures exhibited noticeably higher deviations (approximately 10–15%), indicating the current model’s weaker predictive capability for this metric. The outcomes of this study can benefit contractors and construction equipment manufacturers by improving design efficiency, reducing costs, and enhancing machine performance. Full article

The composition elements of the earthwork allocation system (excavation project, filling project, transfer yard, waste disposal yard, and material yard) and the relationship between material flow were analyzed. Based on the construction of calculation models for soil and water conservation fees, landscape restoration fees, and road transportation intensity, a joint optimization model was constructed with the objectives of minimizing the total allocation cost and minimizing the peak transportation intensity of the road. By dynamically adjusting the volatility, setting penalty factors, and vectorizing NumPy arrays, the ant colony algorithm is improved and the optimization model is solved. Case analysis shows that considering the intensity of road transportation, the peak transportation intensity significantly decreases, and the proportion of directly filled earthwork increases to over 88% without exceeding the capacity of the intermediate transfer site. The total cost only increases by 0.91%, and the allocation plan is more in line with actual construction needs. Full article

The Battle of Rhode Island in 1778 was an important event in the revolutionary war leading to the international recognition of U.S. American independence following the 1776 declaration. It culminated in a month-long campaign against British forces occupying Aquidneck Island, serving as the first combined operation of the newly formed Franco-American alliance. The military fortification at Butts Hill in Portsmouth, Rhode Island, served as a strategic point during the conflict and remains well-conserved today. While LiDAR has assisted in the geospatial surface reconstruction of the site’s earthwork fortifications, it is unknown whether other historically documented buildings within the fort remain preserved underground. We therefore conducted a ground-penetrating radar (GPR) survey to ascertain the presence or absence of architectural features, hypothesizing that GPR imaging could reveal structural remnants from the military barracks constructed in 1777. To test this hypothesis, we used public satellite and LiDAR imagery alongside historical maps to target the location of the historical barracks, creating a grid to survey the area with a GPR module in 0.5 m transects. Our results, superimposing remote sensing imagery with historical maps, indicate that the remains of a barracks building are likely present between circa 5–50 cm beneath today’s surface, warranting future investigations. Full article

Fully weathered phyllite is widely encountered along railway corridors in China, yet its suitability as subgrade fill remains insufficiently documented. This study provides an integrated laboratory and field evaluation of both untreated and low-dosage cement-stabilized phyllite for sustainable transport constructions. Laboratory investigations covered mineralogy, classification, compaction, permeability, compressibility, shear strength, and bearing capacity, while large-scale field trials examined the influence of loose lift thickness, moisture content, and compaction sequence on subgrade quality. Performance indicators included the degree of compaction and the subgrade reaction modulus K₃₀, defined as the plate load modulus measured with a 30 cm diameter plate. A recommended cement dosage of 3.5% (by weight of dry soil) was established based on preliminary trials to balance strength development with construction reliability. The results show that untreated phyllite, when compacted under controlled conditions, can be used in lower subgrade layers, whereas cement stabilization significantly improves strength, stiffness, and constructability, enabling reliable application in the main load-bearing subgrade layers. Beyond mechanical performance, the study demonstrates a methodological innovation by linking laboratory mix design directly with field compaction strategies and embedding these within a life-cycle perspective. The sustainability analysis shows that using stabilized in-situ phyllite achieves lower costs and approximately 30% lower CO₂ emissions compared with importing crushed rock from 30 km away, while promoting resource reuse. Overall, the findings support circular economy and carbon-reduction objectives in railway and road earthworks, offering practical guidance for low-carbon, resource-efficient infrastructure. Full article

Construction sites report the highest rate of industrial accidents, prompting the active development of smart safety management systems based on deep learning-based computer vision technology. To support the digital transformation of construction sites, securing site-specific datasets is essential. In this study, raw data were collected from an actual earthwork site. Key construction equipment and terrain objects primarily operated at the site were identified, and 89,766 images were processed to build a site-specific training dataset. This dataset includes annotated bounding boxes for object detection and polygon masks for instance segmentation. The performance of the dataset was validated using representative models—YOLO v7 for object detection and Mask R-CNN for instance segmentation. Quantitative metrics and visual assessments confirmed the validity and practical applicability of the dataset. The dataset used in this study has been made publicly available for use by researchers in related fields. This dataset is expected to serve as a foundational resource for advancing object detection applications in construction safety. Full article

To overcome the absence of a standardized budget quota system for high-standard farmland projects and the resultant extended compilation cycles and high workloads, this study systematically analyzes quota consumption and innovatively proposes an integrated DEMATEL-ISM-BN and gray clustering analytical model. Through a literature review and engineering feature analysis, a hierarchical factor system was established, encompassing six dimensions (environmental, technical, labor, machinery, material, and management) and 24 indicators. The DEMATEL-ISM method quantified factor weights and structured them into a five-level hierarchy, while Bayesian networks (BNs) enabled probabilistic productivity predictions (29% conservative, 45% moderate, and 26% advanced). Gray clustering was integrated to derive a comprehensive representative consumption value, and validation across six regions demonstrated a comprehensive productivity index of 0.986 (CV = 2.6%) for 17 earthwork projects, confirming model robustness. This research constructs a standardized “factor structure analysis–probabilistic deduction–regional clustering” framework, providing a theoretical foundation for precise budget compilation in high-standard farmland and proposing a novel methodological paradigm for quota consumption research. Full article

Autonomous earthwork machinery is gaining traction as a means to boost productivity and safety on space-constrained urban sites, yet the fast-growing literature has not been fully integrated. To clarify current knowledge, we systematically searched Scopus and screened 597 records, retaining 157 peer-reviewed papers (2015–March 2025) that address autonomy, integrated control, or risk mitigation for excavators, bulldozers, and loaders. Descriptive statistics, VOSviewer mapping, and qualitative synthesis show the output rising rapidly and peaking at 30 papers in 2024, led by China, Korea, and the USA. Four tightly linked themes dominate: perception-driven machine autonomy, IoT-enabled integrated control systems, multi-sensor safety strategies, and the first demonstrations of fleet-level collaboration (e.g., coordinated excavator clusters and unmanned aerial vehicle and unmanned ground vehicle (UAV–UGV) site preparation). Advances include centimeter-scale path tracking, real-time vision-light detection and ranging (LiDAR) fusion and geofenced safety envelopes, but formal validation protocols and robust inter-machine communication remain open challenges. The review distils five research priorities, including adaptive perception and artificial intelligence (AI), digital-twin integration with building information modeling (BIM), cooperative multi-robot planning, rigorous safety assurance, and human–automation partnership that must be addressed to transform isolated prototypes into connected, self-optimizing fleets capable of delivering safer, faster, and more sustainable urban construction. Full article

Earthwork construction presents significant challenges due to its unique characteristics, including irregular topography, inhomogeneous geotechnical properties, dynamic operations involving heavy equipment, and continuous terrain updates over time. Existing methods often fail to accurately capture these complexities, support semantic attributes, simulate realistic equipment–environment interactions, and update the model dynamically during construction. Moreover, most current digital solutions lack an integrated framework capable of linking geotechnical semantics with construction progress in a continuously evolving terrain. This study introduces a novel, voxel-based digital twin framework tailored for earthwork construction. Unlike previous studies that relied on surface, mesh, or layer-based representations, our approach leverages semantically enriched voxelization to encode spatial, material, and behavioral attributes at a high resolution. The proposed framework connects the physical and digital representations of the earthwork environment and is structured into five modules. The data acquisition module gathers terrain, geotechnical, design, and construction data. Virtual models are created for the earthwork in as-planned and as-built models. The digital twin core module utilizes voxels to create a realistic earthwork environment that integrates the as-planned and as-built models, facilitating model–equipment interaction and updating models for progress monitoring. The visualization and simulation module enables model–equipment interaction based on evolving as-built conditions. Finally, the monitoring and analysis module provides volumetric progress insights, semantic material information, and excavation tracking. The key innovation of this framework lies in multi-resolution voxel modeling, semantic mapping of geotechnical properties, and supporting dynamic updates during ongoing construction, enabling model–equipment interaction and material-specific construction progress monitoring. The framework is validated through real-world case studies, demonstrating its effectiveness in providing realistic representations, model–equipment interactions, and supporting progress information and operational insights. Full article

Optimized earth-rock allocation is key in the construction of large-scale navigation channel projects. This paper analyzes the characteristics of a large-scale navigation channel project and establishes an earth-rock allocation system in phases and categories without a transit field. Based on the physical characteristics of the earthwork and stonework used to design a differentiated transport strategy, a synergistic optimization model is built with economic and ecological benefits. As a solution, this paper proposes a sustainable earth-rock allocation optimization method that integrates the improved ant colony algorithm and firefly algorithm, and establishes a two-stage hybrid optimization framework. The application of the Pinglu Canal Project shows that ant colony optimization via division and cooperation combined with the firefly algorithm reduces the transportation cost by 0.128% compared with traditional ant colony optimization; improves the stability by 57.46% (standard deviation) and 59.09% (coefficient of variation) compared with ant colony optimization through division and cooperation; and effectively solves the problems of precocious convergence and local optimization of large-scale earth-rock allocation. It is used to successfully construct an earth-rock allocation model that takes into account the efficiency of the project and the protection of the ecological system in a dynamic environment. Full article

Smart construction technology integrates artificial intelligence, Internet of Things, UAVs, and building information modeling to improve productivity and quality in construction. In road embankment earthworks, ground compaction quality is critical for structural stability and maintenance. This study proposes a methodology combining UAV photogrammetry with intelligent compaction quality management systems to evaluate surface flatness and compaction homogeneity in real-time. High-resolution UAV images were used to generate digital elevation models, from which surface roughness was extracted using terrain element analysis and fast Fourier transform. Local terrain changes were interpreted through contour gradient, outline gradient, and tangential gradient curvature analysis. Field tests were conducted at a pilot site using a vibratory roller, followed by four compaction quality assessments: plate load test, dynamic cone penetration test, light falling weight deflectometer, and compaction meter value. UAV-based flatness analysis revealed that, when surface flatness met the standard, a strong correlation was observed, with results from conventional field tests and intelligent compaction data. The proposed method effectively identified poorly compacted zones and spatial inhomogeneity without interrupting construction. These findings demonstrate that UAV-based terrain analysis can serve as a nondestructive real-time monitoring tool and contribute to automated quality control in smart construction environments. Full article

Excavator operations play a critical role in the productivity of earthworks, yet traditional methods often rely heavily on operators’ intuition and experience, which can lead to inconsistent outcomes. This study investigates how machine guidance (MG) providing real-time feedback relating to excavation depth and slope can modify operators’ actions and improve performance compared with conventional excavation methods. A controlled experiment was conducted at an active construction site, in which four footings were excavated using the two approaches under similar conditions. The results demonstrated that MG excavation reduced the total duration of the work from 3650 s to 2652 s and decreased the number of excavation cycles from 68 to 57, underscoring the impact of timely, precise guidance on efficiency. Moreover, the average fill factor improved from 3.04 under conventional methods to 3.47 with MG, suggesting more consistent and optimal loading of the bucket. These findings confirm that real-time feedback can enhance operator confidence, reduce unnecessary movements, and foster systematic excavation strategies. This study thus provides empirical evidence that MG can significantly optimize excavation performance, highlighting the need for broader adoption of this technology in modern construction practices. Full article