8 research outputs found
Thermomechanical Manipulation of Electric Transport in MoTe2
No full textLayered semimetals such as monoclinic MoTe2 and WTe2 demonstrate superconducting, topological insulating, and Weyl semimetallic states based on their unique electronic band topology. While doping concentration, lattice constants, and spin-orbit coupling can largely modulate the quantum states of the semimetals, a puzzling issue is that their functional carrier density and magnetoresistance for practical applications critically vary by temperature, which cannot be explained by the conventional phonon effect or a structural phase transition. Here, a native doping-mediated thermomechanical manipulation of electric transport in semimetallic MoTe2 is reported, where effective transport is controlled by temperature in an equivalent manner to electric gating. Combining X-ray diffraction, scanning tunneling microscopy, transport measurements, and first-principles calculations, a Fermi level shift and subsequent changes in electronic structures are revealed as the origins of the practical transport changes in MoTe2. Moreover, the initial doping state of the MoTe2, determined by the Te vacancy density in two different growth methods, reciprocally affects the thermomechanical lattice and band structure changes, which is promising for novel electronic applications such as magnetic sensors and memory devices with layered semimetals.
Numerical and Experimental Studies of Mechanical Performance and Structural Enhancement of Industrial Building SSMRs
No full textIn response to the increasing demands of high-technology industrial buildings, renovated standing seam metal roofs (SSMRs) are widely used in the construction of such buildings due to their superior performance regarding heat insulation and waterproofing. However, studies to identify realistic mechanical performance and structural defects in newly applied SSMRs are still limited due to their recent development. In our previous full-scale experiment, the ultimate failure of the roof under wind pressure corresponded to mid-clip failure rather than end clip failure and seam separation; therefore, in this study, the lab-scale experimental programs mainly focused on the mid-clip and the metal roof sheet. Here, the plastic saddle type of the SSMR was chosen as the lab-scale experiment specimen under various loading speeds and angled plastic saddle conditions. The JC material properties were calibrated against experimental results and simulated to predict the dynamic failure response of SSMRs. An additional experimental study was conducted to identify the effect of strengthening SSMRs with wind clips, which showed that 20.77% of the peak load was enhanced after reinforcing the SSMR with wind clips. On the basis of this result, the failure wind speed was computed according to ASCE 7–10 standards with the assumption of a wind clip installed on the corner and edge of the roof panel, indicating that the failure wind speed increased with the wind clip by about 6 to 7 m/s. The current research results suggest a methodology for enhancing the structural performance of renovated industrial building SSMRs
Sustainability of Industrial Building SSMR through Experimental and Analytical Study under Wind Uplift Load
No full textStanding Seam Metal Roofs (SSMRs) are widely used in the construction of industrial buildings, and their structural characteristics are rapidly being changed in order to improve the parameters of heat insulation and waterproofness. However, newly employed SSMRs did not account for a potential structural instability under strong winds by considering the multi-function of SSMRs. In this study, three different types of new SSMRs were chosen as specimens and were used in full-scale experiments, which were performed using the cyclic wind uplift method based on ASTM E1592 regulations. In contrast to a previous study in which the ultimate failure of the roof under wind pressure corresponded to seam line failure due to panel deflection, in this study, the experimental results show that seam separation was induced by a mid-clip rupture. It is verified that the behavior of the mid-clip plays a significant role in the overall performance of SSMRs under wind uplift loading. The objectives of this study were to (i) understand the structural performance and failure mode of new SSMRs under wind uplift pressure, as this condition is closest to reality, and to (ii) quantify the structural sustainability, which can be applied to risk-management practices through the established performance evaluation. It is expected that the present research results may provide future directions for improving the test standards, design guidelines, and risk-management practices
Fragility assessment of installation defects in industrial standing seam metal roof subjected to wind loads
No full textIn industrial structures in which standing seam metal roofs (SSMRs) are commonly used, heat insulation and waterproofing have emerged as crucial requirements for the protection of internal equipment. However, in newly developed SSMRs, the structural systems have become increasingly complex. The installation of insulation layers between the upper and lower panels poses challenges during roof panel installations, resulting in defects owing to the carelessness of the installer. These clip defects can significantly affect the wind-resistance performance of the SSMR structure during testing. In this study, we employed finite element method (FEM) modeling and verification, utilizing the wind resistance test results of SSMRs. In addition, we conducted a variable analysis as well as a fragility assessment focusing on the location and number of clip defects in the SSMRs. The results of this study indicate that the wind performance of the roof was significantly degraded owing to SSMR clip defects. Moreover, the wind resistance performance can be quantitatively evaluated by considering the roof zone and the exposed environment under a wind load
Performance comparison of various time-series forecasting models for bridge sufficiency rating prediction
No full textThe rapid increase in the number of bridges worldwide has intensified the need for effective maintenance strategies to ensure structural safety and economic efficiency. Accurate predictions of future bridge performance are essential for preventing unexpected failures and optimizing road network maintenance planning. However, existing prediction models frequently overlook the time-series characteristics inherent in bridge inspection data, thereby limiting their accuracy. This study aims to develop improved prediction models by integrating sequential data patterns using advanced deep-learning techniques. Data from the National Bridge Inventory were utilized. As most NBI data lacked explicit sequential structures, preprocessing techniques were applied to generate meaningful time-series patterns. Deep-learning models, including deep neural networks (DNNs), convolutional neural networks, long short-term memory (LSTM), and Transformers, were developed and evaluated using cross-validation to optimize their performance. Results showed that the LSTM model improved prediction accuracy by approximately 46% compared to the baseline DNN model. The Transformer model further improved accuracy by approximately 7% over the LSTM, highlighting its superior ability to capture long-term dependencies. These findings highlight the potential of the Transformer model as a powerful tool for predicting bridge performance, thereby supporting effective maintenance planning and reducing the risk of structural failures
Extended limit-collapsed surfaces using fragility analysis of high voltage transmission towers located in coastal areas under wind load
No full textAbstract When evaluating the fragility of structures in response to wind loads, vulnerability analyses are often conducted under intact conditions. Therefore, the actual strength of aged transmission towers may be compromised, resulting in severe damage. Especially for steel structures used over a long period in coastal areas, there is a potential for performance degradation due to corrosion. One of the high-voltage transmission towers, the 765 kV transmission tower, is taller than other towers, making it more vulnerable to strong winds in the event of corrosion. In this study, the structural performance degradation of 765 kV transmission towers in coastal regions based on their service life was investigated. Capacity distributions were provided considering the uncertainties in various parameters, such as the wind attack angle and material properties. A fragility assessment process that accounts for uncertainties in the wind conditions and aerodynamic pa- rameters is proposed. Using the research results, we created limit-collapsed surfaces to evaluate the structural safety of transmission towers based on their service life, wind speed, and wind attack angle. The results showed a quantitative decrease in structural safety due to corrosion depending on the service life, with the most unfavorable wind attack angle being 0°. The proposed limit-collapsed surface can help efficiently evaluate structural conditions considering wind speed, wind attack angle, and service life. Hence, this study can serve as a basis for the structural evaluation of modern transmission towers to avoid power disruptions in major cities.11Nsciescopu
Machine learning-based future performance prediction model for bridge inspection and performance data in South Korea
No full textBridges are vital components of modern transportation infrastructure. The collapse or deterioration of bridges due to aging can disrupt local transportation networks, underscoring the importance of proper maintenance. To address this issue, numerous studies in various countries have focused on predicting the future performance of bridges using inspection data and applying probabilistic, statistical, machine learning, and deep learning techniques. Given that each country has distinct bridge inspection and performance evaluation standards and dataset formats, prediction techniques must be tailored to the specific needs of each country. Following the collapse of the Seongsu Bridge in 1994, South Korea has evaluated bridge performance based on a grading system ranging from A to E, with datasets managed through a Bridge Management System (BMS). In this study, we trained several models, including linear regression, random forest, LightGBM, and deep neural networks, on South Korea’s BMS dataset to develop a component-level grading prediction model. After evaluating their performance, LightGBM, an ensemble model, was selected as the optimal model. This model, tailored to the structure of South Korea’s BMS dataset, demonstrated high performance, with an average accuracy of 80–96% for each component. Using this model, we predicted the future performance of bridges over the next 3 years and found that the number of bridges requiring maintenance, graded as C, tended to increase by 20%. These results provide an intuitive understanding of the changes in bridge performance and grades throughout the bridge lifecycle, contributing to more efficient budget allocation for bridge maintenance in South Korea.11Nsciescopu
Soil compaction from cut-to-length thinning operations in young redwood forests in northern California
No full textIn northern California, United States, a cut-to-length (CTL) system was recently used for the first time to harvest young redwood (Sequoia sempervirens (Lamb. ex D. Don) Endl.) forests. Landowners and public agencies in this region have been concerned about the potential negative impacts of CTL on soils during wet-season harvest operations. To determine soil impacts, we measured changes in soil bulk density (BD) and hydraulic conductivity (HC) after CTL operations in May and August. Soil samples were collected at two locations (track and center) along forwarder trails and at a reference point at three soil depths (0–5, 10–15, and 20–25 cm), and HC samples were collected only at the 0–5 cm soil depth from the same sample points. We found a significant difference in BD between the reference point and track at 0–5 cm, which decreased as soil depth increased. There was a negative correlation between initial BD values and percent increase of BD, supporting the fact that the percent increase in BD was high at the soil surface (25%–30%), but BD did not exceed 1.13 Mg·m–3 at the 0–5 cm depth. However, our HC results were different from what we expected and were not as consistent as the BD results, as the HC data had much higher variability.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author
