Wavelet and Fourier Transforms in Health Monitoring of Embedded Structures

Embedded structures are widely encountered in construction, such as anti-slide piles in a slope and reinforced concrete frames in a building. The embedded structures are vulnerable subjected to seismic effects. After the seismic load, the integrity of the embedded structures is weakened but the damage is unlikely to be directly observed by engineersas a result, damage detection of an embedded structure is critical. In this study, the identification of the structural damage is investigated with Wavelet and Fourier transforms. With these two approaches, the structural signal in different frequency domain can be analyzed. The advantages and disadvantages of these two approaches in identifying the damage locations are compared. An outstanding advantage of the Wavelet transform is that it is able to identify the damage location, which makes this approach attractive for engineering practice. This advantage is exemplified by a cantilever beam finite element model.This research is supported by the National Natural Science foundation of China (Grand No.: 51879203)

Probabilistic Characterization of 3-D Spatial Variability of Soils: Methodology and Strategy

The 3-D spatial variability of soils has significant impacts on the failure mechanism and reliability of geotechnical structures and deserves a quantitative characterization through site investigation. This study develops a probabilistic approach for characterizing the 3-D spatial variability of soils within the framework of maximum likelihood estimation, whose computational problem is addressed through a matrix decomposition technique. The sampling strategy to minimize the statistical uncertainty is explored systematically based on virtual site analysis. The empirical distance criterion and density criterion are proposed to control the statistical uncertainty to a practically acceptable low level.This work was supported by the National Key R&D Program of China (Project No. 2017YFC1501300), the National Natural Science Foundation of China (Project Nos. 51679174, and 51779189), and the Open Fund of Key Laboratory of Rock Mechanics in Hydraulic Structural Engineering, Ministry of Education of China (Project No. RMHSE1903). The financial support is gratefully acknowledged

Probabilistic Analysis of Ground Deformation Induced by Excavation based on Hypoplastic Constitutive Models

Empirical model and finite element method are two commonly-used methods for prediction of ground deformation induced by excavation. Compared with the former, the finite element method can not only predict the deformation of different modes, but also predict the distributed deformation of the whole site. However, results of finite element analysis depends on the constitutive model used in the analysis. This paper uses an advanced hypoplastic constitutive model and its improved edition, which considers the small-strain effect of soil, to represent the soil behavior. Uncertainties are unavoidable in excavation engineering, such as those in soil parameters, loads, and models, etc. These uncertainties have profound effects on the prediction of deformation induced by excavation obtained from the finite element analysis. In order to consider the effect of parameter uncertainty on the prediction results, random variables are used to characterize the parameter uncertainty. Direct Monte Carlo simulation (MCS) method was used to incorporate the parameter uncertainty into reliability analysis of the deformation induced by excavation. The computational costs and convergence issues of finite element method in together with advanced constitutive model result in significant computational challenges in MCS-based reliability analysis. In order to improve the computing efficiency and robustness, artificial neural network (ANN) is adopted as a surrogate model of the finite element method to compute the soil deformation for a given set of uncertain parameters. Results show that responses predicted by the improved hypoplastic model fit the real response better.This work was supported by the National Key R&D Program of China (Project No. 2016YFC0800200), and the National Natural Science Foundation of China (Project Nos. 51579190, 51528901, 51679174), and Young Elite Scientists Sponsorship Program by CAST (Project Nos. 2017QNRC001). The financial support is gratefully acknowledged

Bayesian Updating of Embankment Settlement on Soft Soils with Finite Element Method

Prediction of responses (e.g., embankment settlement) of geotechnical structures on soft soils is a challenging task due to their complex mechanical behaviors. In face with such complexity, the finite element method (FEM) combined with advanced soil constitutive models (e.g., soft soil creep (SSC) model) is frequently used to predict the short-term and long-term responses of geotechnical structures on soft soils, which involves a number of model parameters. Determination of these model parameters depends on knowledge obtained from site investigation data and/or monitoring information. This paper develops a Bayesian sequential updating (BSU) framework that incorporates monitoring information obtained at different construction stages to update FEM model parameters and their corresponding stochastic responses. To address the computational issues in Bayesian analysis, No-UTurn Sampler (NUTS) Markov chain Monte Carlo (MCMC) algorithm is introduced to populate posterior samples, and multiple Hermite response surfaces are constructed for different monitoring phases to reduce the computational efforts costed by evaluating the likelihood function. The proposed method is illustrated by a settlement prediction example of Ballina trial embankment, New South Wales, Australia. Effects of different likelihood functions (namely with and without model bias factor (MBF)) on Bayesian updating of settlement predictions are investigated. Results showed that the proposed BSU framework improves the prediction accuracy of soft soil settlement compared with prior predictions. NUTS is much more efficient in generating posterior samples compared with Metropolis-Hastings (MH) algorithm as the number of model parameters is relatively large. When considering short-term settlement behaviors of soft soils, the likelihood function without MBF is preferred because the adopted SSC can properly characterize short-term behaviors of soft soils. On the other hand, the likelihood function with MBF is recommended because SSC is hard to represent long-term behaviors of soft soils.This work was supported by the National Key R&D Program of China (Project No. 2016YFC0800200), the National Natural Science Foundation of China (Project Nos. 51679174, 51579190, 51528901), and Young Elite Scientists Sponsorship Program by CAST (Project No. 2017QNRC001). The financial support is gratefully acknowledged

Umbilical cord blood: A promising source for allogeneic CAR-T cells

Chimeric antigen receptor T (CAR-T) cell therapy is an effective treatment for relapsed and refractory acute lymphoblastic leukemia (R/R ALL). However, autologous CAR-T cells derived from patients with B-ALL often show poor amplification ability, exhaustion, and anergy. To overcome these limitations, allogeneic CAR-T cells may be used as effective substitutes; however, which source would be the best substitute is unclear. In this study, we compared the immunophenotype and antitumor efficacy of anti-CD19 CAR-T cells derived from healthy donor cord blood (CB), healthy donor peripheral blood (PB), and PB of B-ALL patients [PB (patient)] in vitro and NOD-Prkdcem26cd52Il2rgem26Cd22/Nju (NCG)-immunodeficient mice, respectively. The results revealed that CAR-T cells derived from healthy donor CB and PB showed a higher proportion of naive T cells and longer tumor suppression in tumor-bearing mice than those of PB (patient). PB (patient) CAR-T cells had a higher proportion of regulatory T cells (Treg cells) and released high levels of interluekin-10 (IL-10), which also suggest a poor prognosis. Thus, CAR-T cells derived from healthy donors have better antitumor efficacy than CAR-T cells derived from PB (patient), and CB may be a good source of allogeneic CAR-T cells

Higher Serum Uric Acid Is Associated with Higher Bone Mineral Density in Chinese Men with Type 2 Diabetes Mellitus

Accumulating evidence suggests that oxidative stress is associated with osteoporosis. Serum uric acid (UA) is a strong endogenous antioxidant. Therefore, we investigated the relationship between the serum UA and BMD in Chinese men with T2DM. In this cross-sectional study of 621 men with T2DM, BMDs at lumbar spine (L2–4), femoral neck (FN), and total hip (TH) were measured by dual-energy X-ray absorptiometry (DXA). Serum levels of UA, calcium (Ca), 25-OH vitamin D3 (vitD3), parathyroid hormone (PTH), and creatinine (Cr) were also tested. Data analyses revealed that serum UA levels were positively associated with BMD at all sites (p<0.05) in men with T2DM after adjusting for multiple confounders. The serum UA levels were positively correlated with body weight (r=0.322), body mass index (BMI) (r=0.331), Ca (r=0.179), and Cr (r=0.239) (p<0.001) and were also positively associated with the concentrations of PTH (r=0.10, p<0.05). When compared with those in the lowest tertile of UA levels, men with T2DM in the highest tertile had a lower prevalence of osteoporosis or osteopenia (adjusted odds ratio 0.54, 95% confidence interval [CI] 0.31–0.95). These data suggest that higher serum levels of UA are associated with higher BMDs and lower risks of osteoporosis in Chinese men with T2DM