Long-Period Building Response to Earthquakes in the San Francisco Bay Area

This article reports a study of modeled, long-period building responses to ground-motion simulations of earthquakes in the San Francisco Bay Area. The earthquakes include the 1989 magnitude 6.9 Loma Prieta earthquake, a magnitude 7.8 simulation of the 1906 San Francisco earthquake, and two hypothetical magnitude 7.8 northern San Andreas fault earthquakes with hypocenters north and south of San Francisco. We use the simulated ground motions to excite nonlinear models of 20-story, steel, welded moment-resisting frame (MRF) buildings. We consider MRF buildings designed with two different strengths and modeled with either ductile or brittle welds. Using peak interstory drift ratio (IDR) as a performance measure, the stiffer, higher strength building models outperform the equivalent more flexible, lower strength designs. The hypothetical magnitude 7.8 earthquake with hypocenter north of San Francisco produces the most severe ground motions. In this simulation, the responses of the more flexible, lower strength building model with brittle welds exceed an IDR of 2.5% (that is, threaten life safety) on 54% of the urban area, compared to 4.6% of the urban area for the stiffer, higher strength building with ductile welds. We also use the simulated ground motions to predict the maximum isolator displacement of base-isolated buildings with linear, single-degree-of-freedom (SDOF) models. For two existing 3-sec isolator systems near San Francisco, the design maximum displacement is 0.5 m, and our simulations predict isolator displacements for this type of system in excess of 0.5 m in many urban areas. This article demonstrates that a large, 1906-like earthquake could cause significant damage to long-period buildings in the San Francisco Bay Area

Automated Feature Engineering for Deep Neural Networks with Genetic Programming

Feature engineering is a process that augments the feature vector of a machine learning model with calculated values that are designed to enhance the accuracy of a model’s predictions. Research has shown that the accuracy of models such as deep neural networks, support vector machines, and tree/forest-based algorithms sometimes benefit from feature engineering. Expressions that combine one or more of the original features usually create these engineered features. The choice of the exact structure of an engineered feature is dependent on the type of machine learning model in use. Previous research demonstrated that various model families benefit from different types of engineered feature. Random forests, gradient-boosting machines, or other tree-based models might not see the same accuracy gain that an engineered feature allowed neural networks, generalized linear models, or other dot-product based models to achieve on the same data set. This dissertation presents a genetic programming-based algorithm that automatically engineers features that increase the accuracy of deep neural networks for some data sets. For a genetic programming algorithm to be effective, it must prioritize the search space and efficiently evaluate what it finds. This dissertation algorithm faced a potential search space composed of all possible mathematical combinations of the original feature vector. Five experiments were designed to guide the search process to efficiently evolve good engineered features. The result of this dissertation is an automated feature engineering (AFE) algorithm that is computationally efficient, even though a neural network is used to evaluate each candidate feature. This approach gave the algorithm a greater opportunity to specifically target deep neural networks in its search for engineered features that improve accuracy. Finally, a sixth experiment empirically demonstrated the degree to which this algorithm improved the accuracy of neural networks on data sets augmented by the algorithm’s engineered features

Real-time testing of the on-site warning algorithm in southern California and its performance during the July 29 2008 M_w5.4 Chino Hills earthquake

The real-time performance of the τ_c -P_d on-site early warning algorithm currently is being tested within the California Integrated Seismic Network (CISN). Since January 2007, the algorithm has detected 58 local earthquakes in southern California and Baja with moment magnitudes of 3.0 ≤ M_w ≤ 5.4. Combined with newly derived station corrections the algorithm allowed for rapid determination of moment magnitudes and Modified Mercalli Intensity (MMI) with uncertainties of ±0.5 and ±0.7 units, respectively. The majority of reporting delays ranged from 9 to 16 s. The largest event, the July 29 2008 M_w5.4 Chino Hills earthquake, triggered a total of 60 CISN stations in epicentral distances of up to 250 km. Magnitude predictions at these stations ranged from M_w4.4 to M_w6.5 with a median of M_w5.6. The closest station would have provided up to 6 s warning at Los Angeles City Hall, located 50 km to the west-northwest of Chino Hills

Results of Millikan Library Forced Vibration Testing

This report documents an investigation into the dynamic properties of Millikan Library under forced excitation. On July 10, 2002, we performed frequency sweeps from 1 Hz to 9.7 Hz in both the East-West (E-W) and North-South (N-S) directions using a roof level vibration generator. Natural frequencies were identified at 1.14 Hz (E-W fundamental mode), 1.67 Hz (N-S fundamental mode), 2.38 Hz (Torsional fundamental mode), 4.93 Hz (1st E-Wovertone), 6.57 Hz (1st Torsional overtone), 7.22 Hz (1st N-S overtone), and at 7.83 Hz (2nd E-Wovertone). The damping was estimated at 2.28% for the fundamental E-W mode and 2.39% for the N-S fundamental mode. On August 28, 2002, a modal analysis of each natural frequency was performed using the dense instrumentation network located in the building. For both the E-W and N-S fundamental modes, we observe a nearly linear increase in displacement with height, except at the ground floor which appears to act as a hinge. We observed little basement movement for the E-W mode, while in the N-S mode 30% of the roof displacement was due to basement rocking and translation. Both the E-W and N-S fundamental modes are best modeled by the first mode of a theoretical bending beam. The higher modes are more complex and not well represented by a simple structural system

Constraining fault constitutive behavior with slip and stress heterogeneity

We study how enforcing self-consistency in the statistical properties of the preshear and postshear stress on a fault can be used to constrain fault constitutive behavior beyond that required to produce a desired spatial and temporal evolution of slip in a single event. We explore features of rupture dynamics that (1) lead to slip heterogeneity in earthquake ruptures and (2) maintain these conditions following rupture, so that the stress field is compatible with the generation of aftershocks and facilitates heterogeneous slip in subsequent events. Our three-dimensional finite element simulations of magnitude 7 events on a vertical, planar strike-slip fault show that the conditions that lead to slip heterogeneity remain in place after large events when the dynamic stress drop (initial shear stress) and breakdown work (fracture energy) are spatially heterogeneous. In these models the breakdown work is on the order of MJ/m^2, which is comparable to the radiated energy. These conditions producing slip heterogeneity also tend to produce narrower slip pulses independent of a slip rate dependence in the fault constitutive model. An alternative mechanism for generating these confined slip pulses appears to be fault constitutive models that have a stronger rate dependence, which also makes them difficult to implement in numerical models. We hypothesize that self-consistent ruptures could also be produced by very narrow slip pulses propagating in a self-sustaining heterogeneous stress field with breakdown work comparable to fracture energy estimates of kJ/M^2

A Typology of Antipassives, With Special Reference to Mayan.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Nivolumab-induced fulminant diabetic ketoacidosis followed by thyroiditis

Five days following the 3rd cycle of nivolumab, a monoclonal antibody, which acts as immune checkpoint inhibitor against the programmed cell death protein-1, for metastatic lung adenocarcinoma, a 56-year-old woman presented at the hospital critically ill. On admission, she had severe diabetic ketoacidosis (DKA), as evidenced by venous glucose of 47 mmol/L, blood ketones of 7.5 mmol/L, pH of 6.95 and bicarbonate of 6.6 mmol/L. She has had no personal or family history of diabetes mellitus (DM), while random venous glucose, measured 1 week prior to hospitalisation, was 6.1 mmol/L. On admission, her HbA1c was 8.2% and anti-GAD antibodies were 12 kIU/L (0–5 kU/L), while islet cell antibodies and serum C-peptide were undetectable. Nivolumab was recommenced without the development of other immune-mediated phenomena until 6 months later, when she developed hypothyroidism with TSH 18 U/L and low free T4. She remains insulin dependent and has required levothyroxine replacement, while she has maintained good radiological and clinical response to immunotherapy. This case is notable for the rapidity of onset and profound nature of DKA at presentation, which occurred two months following commencement of immunotherapy. Despite the association of nivolumab with immune-mediated endocrinopathies, only a very small number of patients developing type 1 DM has been reported to date. Patients should be closely monitored for hyperglycaemia and thyroid dysfunction prior to and periodically during immunotherapy

Leech Parasitism of the Gulf Coast Box Turtle, Terrapene carolina major (Testudines:Emydidae) in Mississippi, USA

Ten leeches were collected from a Gulf Coast box turtle, Terrapene carolina major, found crossing a road in Gulfport, Harrison County, Mississippi, USA. Eight of the leeches were identified as Placobdella multilineata and 2 were identified as Helobdella europaea. This represents the second vouchered report of leeches from a box turtle. Helobdella europaea is reported for the first time associated with a turtle and for the second time from the New World

POSE Algorithms for Automated Docking

POSE (relative position and attitude) can be computed in many different ways. Given a sensor that measures bearing to a finite number of spots corresponding to known features (such as a target) of a spacecraft, a number of different algorithms can be used to compute the POSE. NASA has sponsored the development of a flash LIDAR proximity sensor called the Vision Navigation Sensor (VNS) for use by the Orion capsule in future docking missions. This sensor generates data that can be used by a variety of algorithms to compute POSE solutions inside of 15 meters, including at the critical docking range of approximately 1-2 meters. Previously NASA participated in a DARPA program called Orbital Express that achieved the first automated docking for the American space program. During this mission a large set of high quality mated sensor data was obtained at what is essentially the docking distance. This data set is perhaps the most accurate truth data in existence for docking proximity sensors in orbit. In this paper, the flight data from Orbital Express is used to test POSE algorithms at 1.22 meters range. Two different POSE algorithms are tested for two different Fields-of-View (FOVs) and two different pixel noise levels. The results of the analysis are used to predict future performance of the POSE algorithms with VNS data