Experimental and Theoretical Investigation of the Crack Behavior of RC-slabs Subjected to Biaxial Bending

This thesis presented a comprehensive study on the influence of transverse reinforcement on the cracking patterns of RC slabs, including both slab-strips under uniaxial bending and also slabs subjected to biaxial bending

A Comprehensive Review and Analysis of Nanosensors for Structural Health Monitoring in Bridge Maintenance: Innovations, Challenges, and Future Perspectives

This paper presents a thorough review and detailed analysis of nanosensors for structural health monitoring (SHM) in the context of bridge maintenance. With rapid advancements in nanotechnology, nanosensors have emerged as promising tools for detecting and assessing the structural integrity of bridges. The objective of this review is to provide a comprehensive understanding of the various types of nanosensors utilized in bridge maintenance, their operating principles, fabrication techniques, and integration strategies. Furthermore, this paper explores the challenges associated with nanosensor deployment, such as signal processing, power supply, and data interpretation. Finally, the review concludes with an outlook on future developments in the field of nanosensors for SHM in bridge maintenance.publishedVersio

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference On Medical Image Computing Computer Assisted Intervention (MICCAI) 2017. Twenty four valid state-of-the-art liver and liver tumor segmentation algorithms were applied to a set of 131 computed tomography (CT) volumes with different types of tumor contrast levels (hyper-/hypo-intense), abnormalities in tissues (metastasectomie) size and varying amount of lesions. The submitted algorithms have been tested on 70 undisclosed volumes. The dataset is created in collaboration with seven hospitals and research institutions and manually reviewed by independent three radiologists. We found that not a single algorithm performed best for liver and tumors. The best liver segmentation algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.Comment: conferenc

Mechanical properties of excavated soil waste-based cementitious products at normal condition or after water soaked: A literature review of experimental results

Excavated soil waste is one of the construction wastes generated from building foundation pit excavation, tunnel excavation, channel excavation and other engineering excavations. In order to reuse the excavated soil waste in civil engineering, this paper presents a literature review on the feasibility of using excavated soil waste as fine replacements in cementitious products. With the help of existing experiments reported in the literature, a data-driven analysis was conducted on the mechanical properties of excavated soil-based cement mortar, geopolymer, unfired clay bricks and concrete at normal condition (e.g., room temperature and atmospheric moisture) or after water soaked. The influence of excavated soil replacing river sand (i.e., fine aggregate) or binding materials, curing age, thermal treatment, dry sieving process and stabilization process, etc. on the compressive strength, flexural strength and modulus of elasticity of cementitious materials was discussed. The comparative results show that the excavated soil-based products can achieve acceptable mechanical properties when employing physical approaches (e.g., thermal treatment, sieving and grinding) acting on or mixing stabilizer into excavated soil

Automated reconstruction of parametric BIM for bridge based on terrestrial laser scanning data

Building information modeling (BIM) in industrialized bridge construction is usually performed based on initial design information. Differences exist between the model of the structure and its actual geometric dimensions and features due to the manufacturing, transportation, hoisting, assembly, and load bearing of the structure. These variations affect the construction project handover and facility management. The solutions available at present entail the use of point clouds to reconstruct BIM. However, these solutions still encounter problems, such as the inability to obtain the actual geometric features of a bridge quickly and accurately. Moreover, the created BIM is nonparametric and cannot be dynamically adjusted. This paper proposes a fully automatic method of reconstructing parameterized BIM by using point clouds to address the abovementioned problems. An algorithm for bridge point cloud segmentation is developed; the algorithm can separate the bridge point cloud from the entire scanning scene and segment the unit structure point cloud. Another algorithm for extracting the geometric features of the bridge point cloud is also proposed; this algorithm is effective for partially missing point clouds. The feasibility of the proposed method is evaluated and verified using theoretical and actual bridge point clouds, respectively. The reconstruction quality of BIM is also evaluated visually and quantitatively, and the results show that the reconstructed BIM is accurate and reliable

Efficient recycling and reuse of waste concrete on a construction site

status: publishe

A Digital Twin predictive maintenance framework of air handling units based on automatic fault detection and diagnostics

The building industry consumes the most energy globally, making it a priority in energy efficiency initiatives. Heating, ventilation, and air conditioning (HVAC) systems create the heart of buildings. Stable air handling unit (AHU) functioning is vital to ensuring high efficiency and extending the life of HVAC systems. This research proposes a Digital Twin predictive maintenance framework of AHU to overcome the limitations of facility maintenance management (FMM) systems now in use in buildings. Digital Twin technology, which is still at an initial stage in the facility management industry, use Building Information Modeling (BIM), Internet of things (IoT) and semantic technologies to create a better maintenance strategy for building facilities. Three modules are implemented to perform a predictive maintenance framework: operating fault detection in AHU based on the APAR (Air Handling Unit Performance Assessment Rules) method, condition prediction using machine learning techniques, and maintenance planning. Furthermore, the proposed framework was tested in a real-world case study with data between August 2019 and October 2021 for an educational building in Norway to validate that the method was feasible. Inspection information and previous maintenance records are also obtained through the FM system. The results demonstrate that the continually updated data combined with APAR and machine learning algorithms can detect faults and predict the future state of Air Handling Unit (AHU) components, which may assist in maintenance scheduling. Removing the detected operating faults resulted in annual energy savings of several thousand dollars due to eliminating the identified operating faults

Applying BIM and 3D laser scanning technology on virtual pre-assembly for complex steel structure in construction

Steel structure needs to be assembled before the components are transported to the site to ensure the installation of the steel structure successfully. The traditional assembly is a physical assembly method, which needs an amount of equipment, occupies large areas and can be labor and time-consuming. To address these issues, industrial photogrammetry has been developed to acquire data, and the steel structure has been virtually assembled in a virtual environment. The high-precision data acquisition of steel components is now carried out by using 3D laser scanner, and therefore, the feature data of those components can be automatically extracted by programming. As a result, the accurate linear shape of the post-splicing components can be extracted after completing the precise assembly of the steel structure in a virtual environment, which is significant for the subsequent mechanical analysis

Applying BIM and 3D laser scanning technology on virtual pre-assembly for complex steel structure in construction

Steel structure needs to be assembled before the components are transported to the site to ensure the installation of the steel structure successfully. The traditional assembly is a physical assembly method, which needs an amount of equipment, occupies large areas and can be labor and time-consuming. To address these issues, industrial photogrammetry has been developed to acquire data, and the steel structure has been virtually assembled in a virtual environment. The high-precision data acquisition of steel components is now carried out by using 3D laser scanner, and therefore, the feature data of those components can be automatically extracted by programming. As a result, the accurate linear shape of the post-splicing components can be extracted after completing the precise assembly of the steel structure in a virtual environment, which is significant for the subsequent mechanical analysis