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Scour is a critical condition change for a bridge hydraulic system, and terrestrial light detection and ranging (LiDAR) scans have been suggested as a way to quantify the scour conditions. With LiDAR point cloud data, a temporal record of scour can be established. However, there are limitations to LiDAR scans. For example, laser light does not bend and can be obstructed by objects along the light path, resulting in missing geometric information behind the obstacles, thereby creating a void in the point cloud data. To "fill in" the missing data, spatial interpolation of three-dimensional (3D) LiDAR point cloud data using ordinary kriging (OK) is suggested, and actual field data from scanning three scoured bridge piers is presented to demonstrate the application. Kriging is a geostatistical interpolation technique and OK assumes that the spatial variation of the phenomenon or object being considered is random and intrinsically stationary with a constant mean. Here, the complete scour envelope is reconstructed using OK and is shown to have excellent results. [ABSTRACT FROM AUTHOR]

The surge in lithium-ion battery (LIB) use, essential for mass-scale renewable energy storage, raises concerns about fire hazards. However, to date, there is a lack of industry-wide understanding of large-scale LIB fire propagation. This paper suggests a translational forensic approach to promote fire safety awareness and introduces the cellular automata (CA) model coupled with the Monte Carlo (MC) approach to address the complex fire propagation simulation within an energy storage system (ESS). The objective is to demonstrate that the CA-MC model can provide a flexible and scalable connection for all levels of battery fire studies. The numerical model is coupled with experimental tests which have been performed to establish the actual timing of fire propagation from a single source. Cellular automata simulation, conducted through hybrid modeling and an applied risk analysis approach to evaluate fire hazards associated with LIBs, offers crucial insights into potential risks. The results demonstrate that, with fire incident initiation at a probability of 0.1 (10%), 33% of batteries will burn, and at a probability of 0.6 (60%) and beyond, the entire battery module will face complete burndown. Achieving full combustion of the entire module will take only approximately 42 timesteps on average, indicating rapid fire propagation. The actual time for a complete fire to occur in the battery module has been estimated to be 304 s per timestep, or 3.5 h total. Using this example, it is shown that the CA-MC approach can be extended to many other aspects of battery fire studies and is ideal as a translational tool, spanning all domains of the LIB industry. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) geospatial object detection has become essential for 3D geospatial studies driven by explosive growth in 3D data. It is extremely labor- and cost-intensive, though, as it often requires manual detection. Deep learning has been recently used to automate object detection within 3D context. Yet, addressing spatial dependency in 3D data and how it might inform deep learning for 3D geospatial object detection remains a significant challenge. Traditional models focus on the use of spatial properties, often overlooking color and contextual information. Exploiting these nonspatial attributes for 3D geospatial object detection thus becomes crucial. Our study pioneers explicit incorporation of spatial autocorrelation of color information into 3D deep learning for object detection. We introduce an innovative framework to estimate spatial autocorrelation, addressing challenges in unstructured 3D data sets. Our experiments suggest the effectiveness of incorporating spatial autocorrelation features in enhancing the accuracy of 3D deep learning models for geospatial object detection. We further investigate the uncertainty of such contextual information brought by diverse configurations, exemplified by the number of nearest neighbors. This study advances 3D geospatial object detection via using spatial autocorrelation to inform deep learning algorithms, strengthening the connection between GIScience and artificial intelligence and, thus, holding implications for diverse GeoAI applications. [ABSTRACT FROM AUTHOR]

The microtopography of tidal freshwater forested wetlands (TFFWs) impacts biogeochemical processes affecting the carbon and nitrogen dynamics, ecological parameters, and habitat diversity. However, it is challenging to quantify low-relief microtopographic features that might only vary by a few tens of centimeters. We assess the high-resolution fine-scale microtopographic features of a TFFW with terrestrial LiDAR and aerial LiDAR to test a method appropriate to quantify microtopography in low-relief forested wetlands. Our method uses a combination of water-level and elevation thresholding (WALET) to delineate hollows in terrestrial and aerial LiDAR data. Close-range remote sensing technologies can be used for microtopography in forested regions. However, the aerial and terrestrial LiDAR technologies have not been used to analyze or compare microtopographic features in TFFW ecosystems. Therefore, the objectives of this study were (1) to characterize and assess the microtopography of low-relief tidal freshwater forested wetlands and (2) to identify optimal elevation thresholds for widely available aerial LiDAR data to characterize low-relief microtopography. Our results suggest that the WALET method can correctly characterize the microtopography in this area of low-relief topography. The microtopography characterization method described here provides a basis for advanced applications and scaling mechanistic models. [ABSTRACT FROM AUTHOR]

Nondestructive evaluation has been used to investigate construction and use of concrete structures for the nuclear power industry. Nuclear concrete often has unique structural characteristics which increase proclivity towards degradation and inhibit analysis and inspection using traditional nondestructive techniques. Modern embedded sensing technologies can provide opportunities for the in-depth evaluation of nuclear reinforced-concrete structures. This paper offers an assessment of emerging embedded and surficial sensor techniques, and critically evaluates sensor applicability in the analysis of concrete structures used in the nuclear power industry.

This research delves into the intricate dynamics of landslides, emphasizing their consequences on transportation infrastructure, specifically highways and roadway bridges in North Carolina. Based on a prior investigation of bridges in Puerto Rico after Hurricane Maria, we found that bridges above water and situated in valleys can be exposed to both landslide and flooding risks. These bridges faced heightened vulnerability to combined landslides and flooding events due to their low depth on the water surface and the potential for raised flood heights due to upstream landslides. Leveraging a dataset spanning more than a century and inclusive of landslide and bridge information, we employed logistic regression (LR) and random forest (RF) models to predict landslide susceptibility in North Carolina. The study considered conditioning factors such as elevation, aspect, slope, rainfall, distance to faults, and distance to rivers, yielding LR and RF models with accuracy rates of 76.3% and 82.7%, respectively. To establish that a bridge's location is at the bottom of a valley, data including landform, slope, and elevation difference near the bridge location were combined to delineate a bridge in a valley. The difference between bridge height and the lowest river elevation is established as an assumed flooding potential (AFP), which is then used to quantify the flooding risk. Compared to traditional flood risk values, the AFP, reported in elevation differences, is more straightforward and helps bridge engineers visualize the flood risk to a bridge. Specifically, a bridge (NCDOT ID: 740002) is found susceptible to both landslide (92%) and flooding (AFT of 6.61 m) risks and has been validated by field investigation, which is currently being retrofitted by North Carolina DOT with slope reinforcements (soil nailing and grouting). This paper is the first report evaluating the multi-hazard issue of bridges in valleys. The resulting high-fidelity risk map for North Carolina can help bridge engineers in proactive maintenance planning. Future endeavors will extend the analysis to incorporate actual flooding risk susceptibility analysis, thus enhancing our understanding of multi-hazard impacts and guiding resilient mitigation strategies for transportation infrastructure. [ABSTRACT FROM AUTHOR]

This paper reports the rehabilitation of the historic St. Lourdes Church in Tiruchirappalli, India. The structure is a critical landmark and a significant representation of religious buildings in the area. Inspections of the Gothic style masonry church were completed in 1998, where distress and decay due to water damages were identified and repaired using lime-based materials. A subsequent inspection 10 years later indicated that remains a critical issue. This case study demonstrates the importance of rehabilitation of historic structures using appropriate construction techniques including possible alternative rehabilitation approaches to ensure the preservation of these structures. DOI: 10.1061/(ASCE)CF.1943-5509.0000101 CE Database subject headings: Rehabilitation; Restoration; Historic sites; India. Author keywords: Repair; Restoration; St. Lourdes Church.

To ensure power transmission safety, loss of structural integrity of critical transmission facilities due to extreme events, such as strong ground motions induced by blasting, should be avoided. This research intends to establish a vibration safety limit for electric power transmission poles close to blasting events through actual field monitoring and finite-element (FE) simulation of transmission structure dynamic behaviors. In this paper, structural responses of transmission poles during monitored blasting are used to illustrate the methodology for safety limit establishment. The blast response spectra generated based on peak particle velocities were used as a basis for spectrum analysis performed using validated FE models to obtain the structural displacements, reactions, and stress states. The actual recorded ground vibrations as well as modified acceleration time histories were used as input excitations during transient dynamic analysis. A quantitative blast limit was established by comparing structural responses with corresponding design requirements. DOI: 10.1061/(ASCE)CF.1943-5509.0000082 CE Database subject headings: Electric transmission structure; Vibration; Ground motion; Spectral analysis; Blasting; Structural response. Author keywords: Transmission structure; Ground vibration; Spectrum analysis; Time history analysis; Blast limit.

To develop a defense strategy to protect power transmission lines against blast-induced ground motions, an understanding of the dynamic responses of these structures must be first established. This paper presents the results from ground motion monitoring at two blast sites in southern United States. These studies are being conducted as part of a research effort to establish strong ground motion characteristics necessary for developing blast limits for power transmission systems. Ground movements caused by the surface mining blast and quarry blasting were recorded using geophones and wireless triaxial sensing units. The process of establishing the ground motion response spectra via analyzing actual ground motion measurements, including noise filtration and signal processing, is then described. These ground motion response spectra are the necessary inputs for spectrum analysis of structural responses that can be used to establish ground vibration limits. DOI: 10.1061/(ASCE)CF.1943-5509.0000036 CE Database subject headings: Response spectra; Electric transmission structures; Vibration; Measurement; Blasting. Author keywords: Response spectra; Transmission structure; Ground vibration; Measurement; Blast impact.

Soil infiltration is a critical component of most urban runoff models. However, it has been well documented that, during urbanization, soils are greatly modified, especially in relation to soil density. Increased soil compaction results in soils that do not behave in a manner predicted by traditional infiltration models. Laboratory and field tests were conducted to investigate detailed infiltration behavior of disturbed urban soils for a variety of soil textures and levels of compaction. The results from traditional permeability tests on several soil groups showed that, as expected, the degree of compaction greatly affected the steady-state infiltration rate. The field tests highlighted the importance of compaction on the infiltration rate of sandy soils, with minimal effect seen from antecedent moisture conditions. For the clayey soils, however, both the compaction level and antecedent moisture conditions were important in determining the steady-state infiltration rate. CE Database subject headings: Infiltration; Urban areas; Stormwater management; Runoff.