Experimental Evaluation of Compressive Properties of Early-Age Mortar and Concrete Hollow-Block Masonry Prisms within Construction Stages.

Early-age masonry structures require temporary support until they achieve full strength. Nevertheless, there is a limited understanding of the properties of freshly laid masonry and the design of newly constructed, unsupported masonry walls. This situation has led to numerous instances of structural damage and injuries to workers, prompting conservative construction bracing techniques.

Automated crack identification in structures using acoustic waveforms and deep learning.

Structural elements undergo multiple levels of damage at various locations due to environments and critical loading conditions. The level of damage and its location can be predicted using acoustic emission (AE) waveforms that are captured from the generation of inherent microcracks. Existing AE methods are reliant on the feature selection of the captured waveforms and may be subjective in nature.

Augmented Reality-Based Real-Time Visualization for Structural Modal Identification.

In the era of aging civil infrastructure and growing concerns about rapid structural deterioration due to climate change, the demand for real-time structural health monitoring (SHM) techniques has been predominant worldwide. Traditional SHM methods face challenges, including delays in processing acquired data from large structures, time-intensive dense instrumentation, and visualization of real-time structural information. To address these issues, this paper develops a novel real-time visualization method using Augmented Reality (AR) to enhance vibration-based onsite structural inspections.

Machine Learning-Assisted Improved Anomaly Detection for Structural Health Monitoring.

The importance of civil engineering infrastructure in modern societies has increased lately due to the growth of the global economy. It forges global supply chains facilitating enormous economic activity. The bridges usually form critical links in complex supply chain networks.

Data Augmentation for Deep-Learning-Based Multiclass Structural Damage Detection Using Limited Information.

The deterioration of infrastructure's health has become more predominant on a global scale during the 21st century. Aging infrastructure as well as those structures damaged by natural disasters have prompted the research community to improve state-of-the-art methodologies for conducting Structural Health Monitoring (SHM). The necessity for efficient SHM arises from the hazards damaged infrastructure imposes, often resulting in structural collapse, leading to economic loss and human fatalities.

Multivariate empirical mode decomposition-based structural damage localization using limited sensors.

In this article, multivariate empirical mode decomposition is proposed for damage localization in structures using limited measurements. Multivariate empirical mode decomposition is first used to decompose the acceleration responses into their mono-component modal responses. The major contributing modal responses are then used to evaluate the modal energy for the respective modes.

LiDAR-Based Structural Health Monitoring: Applications in Civil Infrastructure Systems.

As innovative technologies emerge, extensive research has been undertaken to develop new structural health monitoring procedures. The current methods, involving on-site visual inspections, have proven to be costly, time-consuming, labor-intensive, and highly subjective for assessing the safety and integrity of civil infrastructures. Mobile and stationary LiDAR (Light Detection and Ranging) devices have significant potential for damage detection, as the scans provide detailed geometric information about the structures being evaluated.

Road Condition Monitoring Using Smart Sensing and Artificial Intelligence: A Review.

Road condition monitoring (RCM) has been a demanding strategic research area in maintaining a large network of transport infrastructures. With advancements in computer vision and data mining techniques along with high computing resources, several innovative pavement distress evaluation systems have been developed in recent years. The majority of these technologies employ next-generation distributed sensors and vision-based artificial intelligence (AI) methodologies to evaluate, classify and localize pavement distresses using the measured data.

Camera trap placement for evaluating species richness, abundance, and activity.

Information from camera traps is used for inferences on species presence, richness, abundance, demography, and activity. Camera trap placement design is likely to influence these parameter estimates. Herein we simultaneously generate and compare estimates obtained from camera traps (a) placed to optimize large carnivore captures and (b) random placement, to infer accuracy and biases for parameter estimates.

Evaluation of spring-loaded cane ground reaction forces during walking using time and frequency domain analysis.

This study examined the effect of a spring-loaded cane stiffness reduction on upper and lower extremity ground reaction forces in time and frequency domain during cane-aided walking. Twenty-nine participants walked five times over two force plates with a spring-loaded cane set at four different stiffnesses and T-scope knee brace set at 30° of knee flexion to simulate a foot injury. Time-frequency domain analyses revealed an increase in ground reaction forces at the simulated injured foot and a decrease in ground reaction forces at the hand via the shaft of the cane for each spring loaded-cane stiffness reduction during walking.