Current Issue : April-June Volume : 2026 Issue Number : 2 Articles : 5 Articles
Structure detection (SD) has emerged as a critical technology for ensuring the safety and longevity of infrastructure, particularly in housing and civil engineering. Traditional SD methods often rely on manual inspections, which are time-consuming, labor-intensive, and prone to human error, especially in complex environments such as dense urban settings or aging buildings with deteriorated materials. Recent advances in autonomous systems— such as Unmanned Aerial Vehicles (UAVs) and climbing robots—have shown promise in addressing these limitations by enabling efficient, real-time data collection. However, challenges persist in accurately detecting and analyzing structural defects (e.g., masonry cracks, concrete spalling) amidst cluttered backgrounds, hardware constraints, and the need for multi-scale feature integration. The integration of machine learning (ML) and deep learning (DL) has revolutionized SD by enabling automated feature extraction and robust defect recognition. For instance, RepConv architectures have been widely adopted for multi-scale object detection, while attention mechanisms like TAM (Technology Acceptance Model) have improved spatial feature fusion in complex scenes. Nevertheless, existing works often focus on singular sensing modalities (e.g., UAVs alone) or neglect the fusion of complementary data streams (e.g., ground-based robot imagery) to enhance detection accuracy. Furthermore, computational redundancy in multi-scale processing and inconsistent bounding box regression in detection frameworks remain underexplored. This study addresses these gaps by proposing a generalized safety inspection system that synergizes UAV and stair-climbing robot data. We introduce a novel multi-scale targeted feature extraction path (Rep-FasterNet TAM block) to unify automated RepConv-based feature refinement with dynamic-scale fusion, reducing computational overhead while preserving critical structural details. For detection, we combine traditional methods with remote sensor fusion to mitigate feature loss during image upsampling/downsampling, supported by a structural model GIOU [Mathematical Definition: GIOU = IOU − (C − U)/C] that enhances bounding box regression through shape/scale-aware constraints and real-time analysis. By siting our work within the context of recent reviews on ML/DL for SD, we demonstrate how our hybrid approach bridges the gap between autonomous inspection hardware and AI-driven defect analysis, offering a scalable solution for large-scale housing safety assessments. In response to challenges in detecting objects accurately during housing safety assessments—including large/dense objects, complex backgrounds, and hardware limitations—we propose a generalized inspection system leveraging data from UAVs and stair-climbing robots. To address multi-scale feature extraction inefficiencies, we design a Rep-FasterNet TAM block that integrates RepConv for automated feature refinement and a multi-scale attention module to enhance spatial feature consistency. For detection, we combine dynamic-scale remote feature fusion with traditional methods, supported by a structural GIOU model that improves bounding box regression through shape/scale constraints and real-time analysis. Experiments demonstrate that our system increases masonry/concrete assessment accuracy by 11.6% and 20.9%, respectively, while reducing manual drawing restoration workload by 16.54%. This validates the effectiveness of our hybrid approach in unifying autonomous inspection hardware with AI-driven analysis, offering a scalable solution for SD in housing infrastructure....
The durability and management of reinforced concrete structures and infrastructures are a central issue in contemporary civil engineering. Efficient structural maintenance has become strategically critical to sustainable land and community management due to aging infrastructure, increasing operational stress, and limited financial resources. This study focuses specifically on reinforced concrete bridge piers, whose fundamental structural role influences road infrastructure management strategies. The objective of this study is to develop and use a system based on convolutional neural networks to visually, rapidly, and automatically identify degraded portions of the reinforcement, based on images acquired on-site or from visual inspections, and classify their level of degradation. The topic addressed is highly innovative. The need to define and calibrate reliable degradation classification criteria, and the difficulty of obtaining images and classifying them correctly for database construction, have influenced the development of the study and make the results interesting and promising, but absolutely preliminary....
Snow construction plays a crucial role in military operations in cold regions, providing tactical fortifications, thermal insulation, and emergency infrastructure in environments where conventional building materials are scarce or require extensive infrastructure for support. Current snow construction methods, including manual piling and compaction, are labor-intensive and inconsistent, limiting their use in large-scale or time-sensitive operations. This study explores the feasibility of adapting a compressed earth block (CEB) machine to produce compressed snow blocks (CSBs) as modular, uniform building units for cold-region applications. Using an AECT Impact 2001A hydraulic press, naturally occurring snow was processed with a snowblower and compacted at maximum operating pressure (i.e., 20,684 kPa) to evaluate block formation, dimensional consistency, and density. The machine successfully produced relatively consistent CSBs, but the initial 3–4 blocks following block height adjustment were generally unsuccessful (e.g., incorrect block height or collapsed/broke) while the machine reached its steady state cyclic condition. These blocks were discarded and excluded from the dataset. The successful CSBs had mean block heights of 7.76 ± 0.56 cm and densities comparable to ice (i.e., 0.83 g/cm3). Variations in block height and mass may be attributed to manual snow loading and minor material impurities. While the dataset is limited, the results warrant further investigation into this technology, particularly regarding CSB strength (i.e., hardness and compressive strength) and performance under variable snow and environmental conditions. Mechanized snow compaction using existing CEB technology is technically feasible and capable of producing uniform, structurally stable CSBs but requires further investigation and modifications to reach its full potential. With design improvements such as automated snow feeding, coldresistant components, and system winterization, this approach could enable scalable CSB production for rapid, on-site construction of snow-based structures in Arctic environments, supporting the military and civilian needs....
Laterally loaded slender piles present a classic soil–structure interaction problem where pile displacements and flexural demands are governed by the mobilized lateral resistance of the surrounding soil and the axial-bending capacity of the reinforced concrete section. In response to increasing pressure to reduce embodied emissions, this study develops LAVERCO, an optimization framework for cost- and CO2-efficient design of bored reinforced-concrete piles in cohesive soils subjected to combined lateral and axial actions. The framework integrates Eurocode-based geotechnical checks with full N–M section verification of the RC pile and applies a genetic algorithm over a multi-parametric grid of lateral load, vertical load, and undrained shear strength, using economic cost and embodied CO2 as alternative single objectives. Rank-based (Spearman) sensitivity analysis quantifies how actions, soil strength, and design variables influence the optimal solutions. The results reveal two consistent geometry regimes: CO2-optimal piles are systematically longer and slimmer, while COST-optimal piles are shorter and thicker. In both cases, the objective is dominated by pile length and is reduced by higher undrained shear strength; vertical load has a moderate direct effect, while horizontal load contributes mainly through deflection and bending checks. Feasibility improves significantly in stronger clays, and CO2-optimal geometries generally incur higher costs, clarifying the trade-off between economic and environmental performance. The framework provides explicit geometry-level guidance for selecting bored pile designs that balance cost and embodied CO2 across a wide range of soil and loading conditions and can be directly applied in both preliminary and detailed designs....
The optimization and evaluation of 3D-printed polylactic acid (PLA) and ABS materials present a promising approach for enhancing the reinforcement of concrete elements, thereby advancing sustainable construction technologies. This study examines the degradation of the structural integrity of 3D-printed PLA- and ABS-reinforced concrete after 28 days of underwater curing. The research focuses on macroscopic analysis and microscopic characterization using a digital microscope and a high-resolution camera to investigate the crystalline structures formed during curing. The findings will offer valuable insights into the structural transformations occurring within concrete elements and potential interactions between concrete and PLA structures, paving the way for future civil engineering applications....
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