Extended Reality (XR) for Condition Assessment of Civil Engineering Structures: A Literature Review

Condition assessment of civil engineering structures has been an active research area due to growing concerns over the safety of aged as well as new civil structures. Utilization of emerging immersive visualization technologies such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) in the architectural, engineering, and construction (AEC) industry has demonstrated that these visualization tools can be paradigm-shifting. Extended Reality (XR), an umbrella term for VR, AR, and MR technologies, has found many diverse use cases in the AEC industry. Despite this exciting trend, there is no review study on the usage of XR technologies for the condition assessment of civil structures. Thus, the present paper aims to fill this gap by presenting a literature review encompassing the utilization of XR technologies for the condition assessment of civil structures. This study aims to provide essential information and guidelines for practitioners and researchers on using XR technologies to maintain the integrity and safety of civil structures.

A partition-based optimization model and its performance benchmark for Generative Anatomy Modeling Language

BACKGROUND

This paper presents a novel iterative approach and rigorous accuracy testing for geometry modeling language - a Partition-based Optimization Model for Generative Anatomy Modeling Language (POM-GAML). POM-GAML is designed to model and create anatomical structures and their variations by satisfying any imposed geometric constraints using a non-linear optimization model. Model partitioning of POM-GAML creates smaller sub-problems of the original model to reduce the exponential execution time required to solve the constraints in linear time with a manageable error.

METHOD

We analyzed our model concerning the iterative approach and graph parameters for different constraint hierarchies. The iteration was used to reduce the error for partitions and solve smaller sub-problems generated by various clustering/community detection algorithms. We empirically tested our model with eleven graph parameters. Graphs for each parameter with increasing constraint sets were generated to evaluate the accuracy of our method.

RESULTS

The average decrease in normalized error with respect to the original problem using cluster/community detection algorithms for constraint sets was above 63.97%. The highest decrease in normalized error after five iterations for the constraint set of 3900 was 70.31%, while the lowest decrease for the constraint set of 3000 was with 63.97%. Pearson correlation analysis between graph parameters and normalized error was carried out. We identified that graph parameters such as diameter, average eccentricity, global efficiency, and average local efficiency showed strong correlations to the normalized error.

CONCLUSIONS

We observed that iteration monotonically decreases the error in all experiments. Our iteration results showed decreased normalized error using the partitioned constrained optimization by linear approximation to the non-linear optimization model.

JanusVF: accurate navigation using SCAAT and virtual fiducials

Several critical limitations exist in the currently available tracking technologies for fully enclosed virtual reality (VR) systems. While several 6DOF tracking projects such as Hedgehog have successfully demonstrated excellent accuracy, precision, and robustness within moderate budgets, these projects still include elements of hardware that can interfere with the user's visual experience. The objective of this project is to design a tracking solution for fully enclosed VR displays that achieves comparable performance to available commercial solutions but without any artifacts that can obscure the user's view. JanusVF is a tracking solution involving a cooperation of both the hardware sensors and the software rendering system. A small, high-resolution camera is worn on the user's head, but faces backward (180 degree rotation about vertical from the user's perspective). After acquisition of the initial state, the VR rendering software draws specific fiducial markers with known size and absolute position inside the VR scene. These virtual markers are only drawn behind the user and in view of the camera. These fiducials are tracked by ARToolkitPlus and integrated by a single-constraint-at-a-time (SCAAT) filter algorithm to update the head pose. Experiments analyzing accuracy, precision, and latency in a six-sided CAVE-like system show performance that is comparable to alternative commercial technologies.

MetNetGE: interactive views of biological networks and ontologies

Background

Linking high-throughput experimental data with biological networks is a key step for understanding complex biological systems. Currently, visualization tools for large metabolic networks often result in a dense web of connections that is difficult to interpret biologically. The MetNetGE application organizes and visualizes biological networks in a meaningful way to improve performance and biological interpretability.

Results

MetNetGE is an interactive visualization tool based on the Google Earth platform. MetNetGE features novel visualization techniques for pathway and ontology information display. Instead of simply showing hundreds of pathways in a complex graph, MetNetGE gives an overview of the network using the hierarchical pathway ontology using a novel layout, called the Enhanced Radial Space-Filling (ERSF) approach that allows the network to be summarized compactly. The non-tree edges in the pathway or gene ontology, which represent pathways or genes that belong to multiple categories, are linked using orbital connections in a third dimension. Biologists can easily identify highly activated pathways or gene ontology categories by mapping of summary experiment statistics such as coefficient of variation and overrepresentation values onto the visualization. After identifying such pathways, biologists can focus on the corresponding region to explore detailed pathway structure and experimental data in an aligned 3D tiered layout. In this paper, the use of MetNetGE is illustrated with pathway diagrams and data from E. coli and Arabidopsis.

Conclusions

MetNetGE is a visualization tool that organizes biological networks according to a hierarchical ontology structure. The ERSF technique assigns attributes in 3D space, such as color, height, and transparency, to any ontological structure. For hierarchical data, the novel ERSF layout enables the user to identify pathways or categories that are differentially regulated in particular experiments. MetNetGE also displays complex biological pathway in an aligned 3D tiered layout for exploration.