State-of-the-art review of automated structural design optimization

The 20th working conference of the IFIP Working Group 7.5 on Reliability and Optimization of Structural Systems (IFIP 2022) will be held at Kyoto University, Kyoto, Japan, September 19-20, 2022.Automated and intelligent structural design optimization has been a heated research topic in structural engineering community. The automated structural design may learn from existing design drawings and finite element models of previous design, infuse design experts' knowledge with design standards and codes, and notably reduce the time and difficulty of structural design compared to conventional approach. This study provides a review of current automated structural design optimization approaches, which may be categorized as the approaches based on finite element model and the deep-learning approach based on human design dataset. For the design optimization based on finite element model, the gradient-based algorithm and gradient-free algorithm are illustrated and compared. The selection of objective function and constraint functions for structural design optimization are summarized. The parallel computing method developed based on high-performance computing resources are also summarized. In addition, the deep learning approaches, which directly generate preliminary structural design drawings based son architectural drawings and datasets of human design results are also summarized. Major architectures in this research field are discussed, including generative adversarial networks (GAN), diffusion models and Variational Auto-Encoder (VAE). The combination between deep learning approach and conventional finite-element model-based approach are also discussed and the future development trends and potential challenges are discussed

Experimental Study on the Dielectric Breakdown Voltage of the Insulating Oil Mixed with Magnetic Nanoparticles

AbstractIn this study, we have measured the dielectric breakdown voltage of transformer oil-based nanofluids in accordance with IEC 156 standard and have investigated the dielectric breakdown performance with the application of an external magnetic field and different volume concentrations of magnetic nanoparticles. It is confirmed that the dielectric breakdown voltage of pure transformer oil is about 10kV with a gap distance of 1mm between electrodes. In the case of our transformer oil-based nanofluids with 0.08% < Φ < 0.39% (Φ means the volume concentration of magnetic nanoparticles in the fluid), the dielectric breakdown voltage is three times higher than that of pure transformer oil. Furthermore, when the external magnetic field is applied under the experimental vessel, the dielectric breakdown voltage of the nanofluids is above 40kV, which is 30% higher than that without the external magnetic field

Efficient Bayesian FFT method for damage detection using ambient vibration data with consideration of uncertainty

Damage detection is one important target in structural health monitoring (SHM). Vibration-based damage detection has attracted more attention in the past decades by tracking the modal parameter changes of objective structures. This paper presents the work on developing a novel Bayesian fast Fourier transform (FFT) method for damage detection using the Bayes factor based on ambient vibration data. Based on the properties of FFT data, the likelihood function and prior probability density function (PDF) can be constructed theoretically based on a Gaussian distribution. The most probable value (MPV) of modal parameters and the associated covariance matrix determined from the ambient vibration data can be integrated into the model developed according to the Bayes factor. A novel damage indicator in the frequency domain is proposed, which can be calculated efficiently using the FFT data and the identified modal parameters. The method is illustrated using synthetic data where a simply supported bridge with 10 elements is simulated. It is found that the damage indicator can identify the damage element in both damage location and extent when moving the sensors installed on the bridge. The proposed method is also applied in a steel truss bridge and an American Society of Civil Engineers (ASCE) benchmark structure. This method can make full use of the FFT data, modal parameters' information, and their posterior uncertainties, providing a new way for future damage detection

Bridge damage detection using probability distribution of RMSE values of moving vehicle acceleration

The 20th working conference of the IFIP Working Group 7.5 on Reliability and Optimization of Structural Systems (IFIP 2022) will be held at Kyoto University, Kyoto, Japan, September 19-20, 2022.In recent years, indirect bridge health monitoring methods using sensors mounted on measuring vehicles, known as drive-by methods, have received increasing attention. This study intends to investigate the feasibility of a drive-by bridge health monitoring method utilizing moving vehicle accelerations. The proposed method investigates whether there is any abnormality in the bridge by using the subtraction between the preliminary-measured vehicle acceleration when the bridge is healthy and the newly-measured vehicle acceleration when the bridge is tested. A band pass filter is applied to the vehicle accelerations before the subtraction in order to eliminate undesirable vibration components other than the frequency of the first bending mode of the bridge. The damage existence and level are investigated by calculating the RMS of the difference between the preliminary-measured and newly-measured accelerations of the vehicle. Considering the variation in the measurements, several measurements are conducted, and the RMS (Root Means Square) values and their probability distributions are examined. The laboratory experiment using a test vehicle equipped with accelerometers was conducted. Observations through this study demonstrated that the proposed method successfully determined the bridge damage existence and its level in a certain accuracy when the frequency of the first mode of the bridge varies with the damage of the bridge

Workforce information database system to support production planning in construction projects

Information on the capacity of available workforce is important in production planning because a production unit's quantitative and qualitative capacities need to match its assigned loads. In the planning process, assigned loads and quantitative capacity are well defined quantitatively through quantity takeoff and database estimation. However, tracking and managing such workforce information, including skills, accident history and experiences, is a challenging task. This research seeks to develop a prototype workforce information database system that defines not only quantitative data but also qualitative measures in relation to load and capacity. A workforce information database system is described for use in workforce-level production planning. This database system tracks the daily quantitative and qualitative production capacities of each worker. The paper also explored the benefits and its applicability in production planning through a survey

Single-Port Transumbilical Laparoscopic-Assisted Adnexal Surgery

Single-port transumbilical laparoscopic-assisted surgery for large, benign adnexal tumors was found to be a feasible alternative to conventional laparoscopic or open surgical methods