Fakultät für Bauingenieurwesen und Geodäsie
https://www.repo.uni-hannover.de/handle/123456789/3
Frei zugängliche Publikationen aus der Fakultät für Bauingenieurwesen und Geodäsie2024-03-19T10:40:23ZAnalysis and design recommendations for structures strengthened by prestressed bonded Fe-SMA
https://www.repo.uni-hannover.de/handle/123456789/16777
Analysis and design recommendations for structures strengthened by prestressed bonded Fe-SMA
Li, Lingzhen; Wang, Sizhe; Chatzi, Eleni; Motavalli, Masoud; Ghafoori, Elyas
Previous studies have demonstrated a great potential of prestressed strengthening of structures employing iron-based shape memory alloys (Fe-SMAs). A bonded Fe-SMA strengthening solution with partial activation has been proposed. However, an analytical model for assessing the strengthening efficiency was lacking, due to the unique nature of the employed prestressing mechanism involving heating. In this study, a symmetric strengthening model and an asymmetric strengthening model are developed to analyze the prestress level in steel and glass beams and plates strengthened by bonded Fe-SMA strips. The asymmetric strengthening model is then modified to analyze reinforced concrete (RC) beams strengthened by embedded Fe-SMA rebars. Recovery stress at different activation temperatures, the influence of the activation temperature on the adhesive bond, as well as the prestress loss resulting from the deformation of substrate elements and adhesive joints are taken into account. The predicted strains and deflections in the parent structure closely approximate the experimental measurements that appear in current literature. A parametric study and a sensitivity analysis are then conducted to assess the impact of the four influential features on the final prestress level, and their impact is ranked in the following order: recovery stress ≈ Fe-SMA width > activation length > bonded anchorage length. Based on these findings, a design strategy, in line with Eurocode 0, for the bonded/embedded Fe-SMA strengthening system is proposed. Finally, some perspectives on potential areas for future research are offered.
2024-01-01T00:00:00ZSpatial and Temporal Patterns of Land Subsidence and Sinkhole Occurrence in the Konya Endorheic Basin, Turkey
https://www.repo.uni-hannover.de/handle/123456789/16778
Spatial and Temporal Patterns of Land Subsidence and Sinkhole Occurrence in the Konya Endorheic Basin, Turkey
Orhan, Osman; Haghshenas Haghighi, Mahmud; Demir, Vahdettin; Gökkaya, Ergin; Gutiérrez, Francisco; Al-Halbouni, Djamil
The endorheic Konya Basin is a vast aggradational plain in Central Anatolia, Türkiye. It occupies a significant portion of Konya Province, covering approximately 50,000 km2. The basin is subjected to intense groundwater withdrawal and extensive agricultural activities with excessive irrigation. These activities have led to human-induced hazards, such as sinkholes and regional land subsidence. Although sinkhole occurrence mainly occurs in the Karapınar area, land subsidence is primarily observed in the central sector of Konya city, with 2 million inhabitants, as well as in various parts of the basin. This study focuses on determining the extent and rate of land subsidence throughout the basin, understanding sinkhole formation, and unraveling their relationship with anthropogenic activities. For this purpose, Interferometric Synthetic Aperture Radar (InSAR) analysis of Sentinel-1 data from 2014 to 2022 was conducted to identify and assess land subsidence. We also used the land cover data and groundwater-level information to better understand the spatial and temporal patterns of land subsidence and sinkhole occurrence. Additionally, the land cover data were used to resolve spatial–temporal variations in the cultivated area and urbanization, which are the main factors governing groundwater exploitation in the region. Our study identified widespread subsidence zones with rates as high as 90 mm/y. Groundwater overexploitation to sustain extensive agricultural operations is the main cause of the high rate of land subsidence. Additionally, it was discovered that the number of sinkholes has substantially increased due to anthropogenic influences, currently amounting to as many as 660.
2024-01-01T00:00:00ZThermal anomaly detection based on saliency analysis from multimodal imaging sources
https://www.repo.uni-hannover.de/handle/123456789/16757
Thermal anomaly detection based on saliency analysis from multimodal imaging sources
Sledz, A.; Heipke, C.
Thermal anomaly detection has an important role in remote sensing. One of the most widely used instruments for this task is a Thermal InfraRed (TIR) camera. In this work, thermal anomaly detection is formulated as a salient region detection, which is motivated by the assumption that a hot region often attracts attention of the human eye in thermal infrared images. Using TIR and optical images together, our working hypothesis is defined in the following manner: a hot region that appears as a salient region only in the TIR image and not in the optical image is a thermal anomaly. This work presents a two-step classification method for thermal anomaly detection based on an information fusion of saliency maps derived from both, TIR and optical images. Information fusion, based on the Dempster-Shafer evidence theory, is used in the first phase to find the location of regions suspected to be thermal anomalies. This classification problem is formulated as a multi-class problem and is carried out in an unsupervised manner on a pixel level. In the following phase, classification is formulated as a binary region-based problem in order to differentiate between normal temperature variations and thermal anomalies, while Random Forest (RF) is chosen as the classifier. In the seconds phase, the classification results from the previous phase are used as features along with temperature information and height details, which are obtained from a Digital Surface Model (DSM). We tested the approach using a dataset, which was collected from a UAV with TIR and optical cameras for monitoring District Heating Systems (DHS). Despite some limitations outlined in the paper, the presented innovative method to identify thermal anomalies has achieved up to 98.7 percent overall accuracy.
2021-01-01T00:00:00ZAdversarial discriminative domain adaptation for deforestation detection
https://www.repo.uni-hannover.de/handle/123456789/16758
Adversarial discriminative domain adaptation for deforestation detection
Noa, J.; Soto, P.J.; Costa, G.A.O.P.; Wittich, D.; Feitosa, R.Q.; Rottensteiner, F.
Although very efficient in a number of application fields, deep learning based models are known to demand large amounts of labeled data for training. Particularly for remote sensing applications, responding to that demand is generally expensive and time consuming. Moreover, supervised training methods tend to perform poorly when they are tested with a set of samples that does not match the general characteristics of the training set. Domain adaptation methods can be used to mitigate those problems, especially in applications where labeled data is only available for a particular region or epoch, i.e., for a source domain, but not for a target domain on which the model should be tested. In this work we introduce a domain adaptation approach based on representation matching for the deforestation detection task. The approach follows the Adversarial Discriminative Domain Adaptation (ADDA) framework, and we introduce a margin-based regularization constraint in the learning process that promotes a better convergence of the model parameters during training. The approach is evaluated using three different domains, which represent sites in different forest biomes. The experimental results show that the approach is successful in the adaptation of most of the domain combination scenarios, usually with considerable gains in relation to the baselines.
2021-01-01T00:00:00Z