Since the Fourth Industrial Revolution, existing manpower-centric manufacture has been shifting towards technology and data-centric production in all areas of society. The construction sector is also facing a new paradigm called smart construction with a clear purpose of improving productivity and securing safety by applying site management using information and communications technology (ICT). This study aims to develop a framework for earthwork process digitalization based on images acquired by using the unmanned aerial system (UAS). The entire framework includes precise UAS data acquisition, cut-and-fill volume estimation, cross-section drawing, and geo-fencing generation. To this end, homogeneous time-series drone image data were obtained from active road construction sites under earthwork. The developed system was able to generate precise 3D topographical models and estimate cut-and-fill volume changes. In addition, the proposed framework generated cross-sectional views of each area of interest throughout the construction stages and finally created geo-fencing to assist the safe operation of heavy equipment. We expect that the proposed framework can contribute to smart construction areas by automating the process of digitizing earthwork progress.

In this study, to reduce the use of cement and sand, porous feldspar with excellent economic efficiency was used as a substitute in the heat storage concrete layer. When porous feldspar and four other silicate minerals were used as substitute materials for sand in cement mortar, the specimen with the porous feldspar exhibited approximately 16–63% higher compressive strength, thereby exhibiting a higher reactivity with cement compared to the other minerals. To compensate for the reduction in strength owing to the decreased cement content, mechanical and chemical activation methods were employed. When the specific surface area of porous feldspar was increased, the unit weight was reduced by approximately 30% and the compressive strength was increased by up to 90%. In addition, the results of the thermal diffusion test confirmed that thermal diffusion increased owing to a reduction in the unit weight; the heat storage characteristics improved owing to the better porosity of feldspar. When chemical activation was performed after reducing the cement content by 5% and replacing the sand with porous feldspar, the compressive strength was found to be approximately twice that of an ordinary cement mortar. In a large-scale model experiment, the heat storage layer containing the porous feldspar exhibited better heat conduction and heat storage characteristics than the heat storage layer composed of ordinary cement mortar. Additionally, energy savings of 57% were observed.

In road construction, the compaction process using vibratory rollers is essential to increase the ground stiffness in earthworks. There need to be clear standards regarding the operation of compaction rollers during compaction work. Various simple quality inspection techniques have been developed to check the stiffness of the ground, but plate load test and field density test are the most commonly used test methods to evaluate the degree of compaction during road construction. However, both inspection methods could be more efficient, as they cannot be performed in all sections due to time and cost. Additionally, in compaction work today, the worker will individually judge the number of compactions, thickness, speed, vibration etc. based on his or her own experience. This means that the quality of compaction varies depending on the worker. In this study, quality inspection results for all sections were obtained using an intelligent quality control system that employs an IC roller, a technology that is now commercially available. The effect of the operating conditions of the vibrating roller (roller compaction direction, compaction roller speed, and compaction roller vibration method) on the compaction quality was analyzed using the intelligent quality control value. Through our highway construction site tests, it was found that the speed of the compaction roller and the vibration method of the compaction roller affected the degree of compaction, but the direction of compaction did not. Therefore, if the compaction work is performed by adjusting the driving speed and vibration method of the vibrating roller according to the ground conditions, repetitive compaction work can be reduced, thereby reducing construction costs and lowering work time, which will achieve an improved work efficiency.

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.

We experimentally demonstrate that linear equalization combined with MLSE detection can reduce requirements for sampling speed and number of states versus a stand-alone MLSE detector without linear equalization.