Fernando Moreu

Mahsa Sanei, Fernando Moreu

Rebar inspection in reinforced concrete construction requires sustained awkward postures and complex mental mapping of two-dimensional drawings onto three-dimensional assemblies. This study evaluated an Augmented Reality (AR)-assisted rebar inspection system deployed on Microsoft HoloLens 2 through a within-subjects experiment with 30 participants. Full-body kinematics were recorded using a motion capture system at 100 Hz while participants performed traditional and AR-assisted spacing inspection. AR reduced mean trunk flexion by 30.8%, mean neck flexion by 32.8%, and task completion time by 67.7%. Walking distance and hand-path length each decreased by over 50%. NASA Task Load Index scores decreased by 45.6% overall, with the largest reduction in physical demand. Inspection accuracy was maintained across conditions. The System Usability Scale yielded a mean score of 76.1 with 83% of participants rating the system acceptable. These results provide convergent objective and subjective evidence that AR-assisted inspection reduces ergonomic risk and perceived workload maintaining inspection quality.

Kaveh Malek, Fernando Moreu

Convolutional Neural Network (CNN) models often lack the ability to incorporate human input, which can be addressed by Augmented Reality (AR) headsets. However, current AR headsets face limitations in processing power, which has prevented researchers from performing real-time, complex image recognition tasks using CNNs in AR headsets. This paper presents a method to deploy CNN models on AR headsets by training them on computers and transferring the optimized weight matrices to the headset. The approach transforms the image data and CNN layers into a one-dimensional format suitable for the AR platform. We demonstrate this method by training the LeNet-5 CNN model on the MNIST dataset using PyTorch and deploying it on a HoloLens AR headset. The results show that the model maintains an accuracy of approximately 98%, similar to its performance on a computer. This integration of CNN and AR enables real-time image processing on AR headsets, allowing for the incorporation of human input into AI models.

Jiaqi Xu, Elijah Wyckoff, John-Wesley Hanson et al.

In recent years, augmented reality (AR) technology has been increasingly employed in structural health monitoring (SHM). In the case of conditions following a seismic event, inspections are conducted to evaluate the progression of the damage pattern quantitatively and efficiently respond if the displacement pattern is determined to be unsafe. Additionally, quantification of nearby structural changes over short-term and long-term periods can provide building inspectors with information to improve safety. This paper proposes the Time Machine Measure (TMM) application on an Augmented Reality (AR) Head-Mounted-Device (HMD) platform. The main function of the TMM application is to restore the saved meshes of a past environment and overlay them onto the real environment so that inspectors can intuitively measure structural deformation and other movement across time. The proposed TMM application was verified by experiments meant to simulate a real-world inspection.

Jiaqi Xu, Derek Doyle, Fernando Moreu

Augmented Reality (AR) is a technology superimposing interactional virtual objects onto a real environment. Since the beginning of the millennium, AR technologies have shown rapid growth, with significant research publications in engineering and science. However, the civil infrastructure community has minimally implemented AR technologies to date. One of the challenges that civil engineers face when understanding and using AR is the lack of a classification of AR in the context of capabilities for civil infrastructure applications. Practitioners in civil infrastructure, like most engineering fields, prioritize understanding the level of maturity of a new technology before considering its adoption and field implementation. This paper compares the capabilities of sixteen AR Head-Mounted Devices (HMDs) available in the market since 2017, ranking them in terms of performance for civil infrastructure implementations. Finally, the authors recommend a development framework for practical AR interfaces with civil infrastructure and operations.

Roya Nasimi, Fernando Moreu, John Stormont

Rockfalls are a hazard for the safety of infrastructure as well as people. Identifying loose rocks by inspection of slopes adjacent to roadways and other infrastructure and removing them in advance can be an effective way to prevent unexpected rockfall incidents. This paper proposes a system towards an automated inspection for potential rockfalls. A robot is used to repeatedly strike or tap on the rock surface. The sound from the tapping is collected by the robot and subsequently classified with the intent of identifying rocks that are broken and prone to fall. Principal Component Analysis (PCA) of the collected acoustic data is used to recognize patterns associated with rocks of various conditions, including intact as well as rock with different types and locations of cracks. The PCA classification was first demonstrated simulating sounds of different characteristics that were automatically trained and tested. Secondly, a laboratory test was conducted tapping rock specimens with three different levels of discontinuity in depth and shape. A real microphone mounted on the robot recorded the sound and the data were classified in three clusters within 2D space. A model was created using the training data to classify the reminder of the data (the test data). The performance of the method is evaluated with a confusion matrix.

Elijah Wyckoff, Marlan Ball, Fernando Moreu

Operators want to maintain awareness of the structure being tested while observing sensor data. Normally the human's gaze shifts to a separate device or screen during the experiment for data information, missing the structure's physical response. The human-computer interaction provides valuable data and information but separates the human from the reality. The sensor data does not collect experiment safety, quality, and other contextual information of critical value to the operator. To solve this problem, this research provides humans with real-time information about vibrations using an Augmented Reality (AR) application. An application is developed to augment sensor data on top of the area of interest, which allows the user to perceive real-time changes that the data may not warn of. This paper presents the results of an experiment that show how AR can provide a channel for direct sensor feedback while increasing awareness of reality. In the experiment a researcher attempts to closely follow a moving sensor with their own sensor while observing the moving sensor's data with and without AR. The results of the reported experiment indicate that augmenting the information collected from sensors in real-time narrows the operator's focus to the structure of interest for more efficient and informed experimentation.