Search Results (4)

Built environment elements management involving heritage buildings requires a nuanced approach that balances cultural preservation, planning efficiency, and resource optimization. Conventional evaluation methods frequently neglect public perception, leading to misaligned priorities and ineffective heritage resource deployment. To address this gap, this study proposes a Sandglass Tiered Model that integrates public perception into the value assessment process of culturally significant buildings. By integrating multi-source perception data and cultural ontology through a structured, tiered data collection mechanism, the model translates subjective views into four quantifiable indices: symbolizability, authenticity, readability, and regionality. These indices form the basis of an AHP-GRA–driven assessment framework, facilitating value-based prioritization and spatial zoning of heritage elements within construction projects. The model was empirically validated in the Yiling Cultural Heritage Area, where it effectively facilitated differentiated building strategies, optimized resource sequencing, and improved alignment between project goals and stakeholder expectations. Importantly, the model provides a transferable framework that embeds cultural awareness into the lifecycle of heritage building projects—from pre-design evaluation to renovation and adaptive reuse. By integrating public perception into construction workflows, this approach provides a dynamic and participatory framework for managing complex heritage assets within urban development contexts. It improves the precision, responsiveness, and cultural sensitivity of construction planning, offering practical insights for policymakers, architects, and construction managers working in resource-intensive or culturally rich environments. Full article

Achieving the real mechanical performance of construction materials is significantly important for the design and engineering of structures. However, previous researchers have shown that contact friction performs an important role in the results of uniaxial compression tests. Strong discreteness generally appears in concrete-like construction materials due to the random distribution of the components. A numerical meso-scale finite-element (FE) method provides the possibility of generating an ideal material with the same component percentages and distribution. Thus, a well-designed meso-FE model was employed to investigate the effect of friction on the mechanical behavior and failure characteristics of concrete under uniaxial compression loading. The results showed that the mechanical behavior and failure profiles of the simulation matched well with the experimental results. Based on this model, the effect of friction was determined by changing the contact friction coefficient from 0.0 to 0.7. It was found that frictional contact had a slight influence on the elastic compressive mechanical behavior of concrete. However, the nonlinear hardening behavior of the stress–strain curves showed a fairly strong relationship with the frictional contact. The final failure profiles of the experiments showed a “sand-glass” shape that might be expected to result from the contact friction. Thus, the numerical meso-scale FE model showed that contact friction had a significant influence on both the mechanical performance and the failure profiles of concrete. Full article

Existing object detection methods with many parameters and computations are not suitable for deployment on devices with poor performance in agricultural environments. Therefore, this study proposes a lightweight crop pest detection method based on convolutional neural networks, named YOLOLite-CSG. The basic architecture of the method is derived from a simplified version of YOLOv3, namely YOLOLite, and k-means++ is utilized to improve the generation process of the prior boxes. In addition, a lightweight sandglass block and coordinate attention are used to optimize the structure of residual blocks. The method was evaluated on the CP15 crop pest dataset. Its detection precision exceeds that of YOLOv3, at 82.9%, while the number of parameters is 5 million, only 8.1% of the number used by YOLOv3, and the number of computations is 9.8 GFLOPs, only 15% of that used by YOLOv3. Furthermore, the detection precision of the method is superior to all other commonly used object detection methods evaluated in this study, with a maximum improvement of 10.6%, and it still has a significant edge in the number of parameters and computation required. The method has excellent pest detection precision with extremely few parameters and computations. It is well-suited to be deployed on equipment for detecting crop pests in agricultural environments. Full article

In an orchard environment with a complex background and changing light conditions, the banana stalk, fruit, branches, and leaves are very similar in color. The fast and accurate detection and segmentation of a banana stalk are crucial to realize the automatic picking using a banana picking robot. In this paper, a banana stalk segmentation method based on a lightweight multi-feature fusion deep neural network (MFN) is proposed. The proposed network is mainly composed of encoding and decoding networks, in which the sandglass bottleneck design is adopted to alleviate the information a loss in high dimension. In the decoding network, a different sized dilated convolution kernel is used for convolution operation to make the extracted banana stalk features denser. The proposed network is verified by experiments. In the experiments, the detection precision, segmentation accuracy, number of parameters, operation efficiency, and average execution time are used as evaluation metrics, and the proposed network is compared with Resnet_Segnet, Mobilenet_Segnet, and a few other networks. The experimental results show that compared to other networks, the number of network parameters of the proposed network is significantly reduced, the running frame rate is improved, and the average execution time is shortened. Full article