Incorporating a multiple discrete-continuous outcome in the generalized heterogeneous data model: Application to residential self-selection effects analysis in an activity time-use behavior model

This paper makes both a methodological contribution as well as an empirical contribution. From a methodological perspective, we propose a new econometric approach for the estimation of joint mixed models that include a multiple discrete choice outcome and a nominal discrete outcome, in addition to the count, binary/ordinal outcomes, and continuous outcomes considered in traditional structural equation models. These outcomes are modeled together by specifying latent underlying unobserved individual lifestyle, personality, and attitudinal factors that impact the many outcomes, and generate the jointness among the outcomes. From an empirical perspective, we analyze residential location choice, household vehicle ownership choice, as well as time-use choices, and investigate the extent of association versus causality in the effects of residential density on activity participation and mobility choices. The sample for the empirical application is drawn from a travel survey conducted in the Puget Sound Region in 2014. The results show that residential density effects on activity participation and motorized auto ownership are both associative as well as causal, emphasizing that accounting for residential self-selection effects are not simply esoteric econometric pursuits, but can have important implications for land-use policy measures that focus on neo-urbanist design

Efecto de cultivos hospederos y no hospederos sobre propágulos micorrícicos arbusculares

Recent field studies have shown that fungal spores decrease when non host plants are used as a pre-culture. The objective of this study was to evaluate how host plant like oats (Avena sativa L.), and non host, as lupine (Lupinus albus L.), and rapeseed (Brassica napus L.) can influence on arbuscular mycorrhizal fungi (AMF) propagules diversity and phosphatase activity when growing in an Andisol and an Inceptisol. The trial was conducted from September 2006 through March 2007 in greenhouse conditions using 5 kg pots with in a completely randomized design with four replicates. The number of AMF spores was higher in the Andisol than the Inceptisol and highest when using oats than lupine or raps as plant host. Oats also showed a large enrichment of morphotypes, whereas lupine and raps were poor. The phosphatase activity (P-ase) in Inceptisol was lower than in Andisol and between crops increased in the order oats < rapeseed < lupine; while significant differences between lupines with other crops were registred. Again, our results reinforce the hypothesis that the mycorrhizae and phosphatase activity are complementary mechanisms developed by plants for a better P acquisition.Estudios recientes en campo han demostrado que las esporas fúngicas disminuyen cuando se utilizan como pre-cultivo no hospederos. El objetivo de este trabajo fue estudiar la influencia de un cultivo hospedero, avena (Avena sativa L.) y cultivos no hospederos, como lupino (Lupinus albus L.) y raps (Brassica napus L.) sobre los propágulos de hongos micorrícicos arbusculares (HMA) y diversidad fúngica junto con la actividad fosfatásica en un Andisol serie Temuco e Inceptisol serie Lumaco. El ensayo se realizó en condiciones de invernadero, desde septiembre de 2006 hasta marzo de 2007, utilizándose macetas de 5 kg con un diseño experimental completamente al azar con cuatro repeticiones. El número de esporas HMA fue mayor en el Andisol que en el Inceptisol y mayor cuando se usó avena como planta hospedera que lupino y raps. La avena también mostró una gran riqueza de morfotipos mientras que, en lupino y raps fue baja. La actividad fosfatásica en el Inceptisol fue menor que en el Andisol y entre cultivos aumentó en orden avena < raps < lupino; mientras que, se encontraron diferencias significativas entre lupino con los otros cultivos. Nuevamente, nuestros resultados refuerzan la hipótesis que las micorrizas y actividad fosfatásica son mecanismos complementarios que utiliza la planta para una mejor captación de fósfor

Amphibious Seismic Survey Images Plate Interface at 1960 Chile Earthquake

The southern central Chilean margin at the site of the largest historically recorded earthquake in the Valdivia region, in 1960 (Mw = 9.5), is part of the 5000-km-long active subduction system whose geodynamic evolution is controversially debated and poorly understood. Covering the area between 36° and 40°S, the oceanic crust is segmented by prominent fracture zones. The offshore forearc and its onshore continuation show a complex image with segments of varying geophysical character, and several fault systems active during the past 24 m.y. In autumn 2001, the project SPOC was organized to study the Subduction Processes Off Chile, with a focus on the seismogenic coupling zone and the forearc. The acquired seismic data crossing the Chilean subduction system were gathered in a combined offshore-onshore survey and provide new insights into the lithospheric structure and evolution of active margins with insignificant frontal accretion

A Joint Count-Continuous Model of Travel Behavior with Selection Based on a Multinomial Probit Residential Density Choice Model

At the time of publication Chandra R. Bhat, Sebastian Astroza, and Raghuprasad Sidharthan were at the University of Texas at Austin, and Mohammad Jobair Bin Alam and Waleed H. Khushefati were at King Abdulaziz University.This paper formulates a multidimensional choice model system that is capable of handling multiple nominal variables, multiple count dependent variables, and multiple continuous dependent variables. The system takes the form of a treatment-outcome selection system with multiple treatments and multiple outcome variables. The Maximum Approximate Composite Marginal Likelihood (MACML) approach is proposed in estimation, and a simulation experiment is undertaken to evaluate the ability of the MACML method to recover the model parameters in such integrated systems. These experiments show that our estimation approach recovers the underlying parameters very well and is efficient from an econometric perspective. The parametric model system proposed in the paper is applied to an analysis of household-level decisions on residential location, motorized vehicle ownership, the number of daily motorized tours, the number of daily non-motorized tours, and the average distance for the motorized tours. The empirical analysis uses the NHTS 2009 data from the San Francisco Bay area. Model estimation results show that the choice dimensions considered in this paper are inter-related, both through direct observed structural relationships and through correlations across unobserved factors (error terms) affecting multiple choice dimensions. The significant presence of self-selection effects (endogeneity) suggests that modeling the various choice processes in an independent sequence of models is not reflective of the true relationships that exist across these choice dimensions, as also reinforced through the computation of treatment effects in the paper.Civil, Architectural, and Environmental Engineerin

Bayesian Model-Updating Implementation in a Five-Story Building

Simplifications and theoretical assumptions are usually incorporated into the numerical modeling of structures. However, these assumptions may reduce the accuracy of the simulation results. This problem has led to the development of model-updating techniques to minimize the error between the experimental response and the modeled structure by updating its parameters based on the observed data. Structural numerical models are typically constructed using a deterministic approach, whereby a single best-estimated value of each structural parameter is obtained. However, structural models are often complex and involve many uncertain variables, where a unique solution that captures all the variability is not possible. Updating techniques using Bayesian Inference (BI) have been developed to quantify parametric uncertainty in analytical models. This paper presents the implementation of the BI in the parametric updating of a five-story building model and the quantification of its associated uncertainty. The Bayesian framework is implemented to update the model parameters and calculate the covariance matrix of the output parameters based on the experimental information provided by modal frequencies and mode shapes. The main advantage of this approach is that the uncertainty in the experimental data is considered by defining the likelihood function as a multivariate normal distribution, leading to a better representation of the actual building behavior. The results showed that this Bayesian model-updating approach effectively allows a statistically rigorous update of the model parameters, characterizing the uncertainty and increasing confidence in the model’s predictions, which is particularly useful in engineering applications where model accuracy is critical

Bayesian optimal estimation for output-only nonlinear system and damage identification of civil structures

This paper presents a new framework for output-only nonlinear system and damage identification of civil structures. This framework is based on nonlinear finite element (FE) model updating in the time-domain, using only the sparsely measured structural response to unmeasured or partially measured earthquake excitation. The proposed framework provides a computationally feasible approach for structural health monitoring and damage identification of civil structures when accurate measurement of the input seismic excitations is challenging (e.g., buildings with significant foundation rocking and bridges with piers in deep water) or the measured seismic excitations are erroneous and/or distorted by significant measurement error (e.g., malfunctioning sensors). Grounded on Bayesian inference, the proposed framework estimates the unknown FE model parameters and the ground acceleration time histories simultaneously, using the sparse measured dynamic response of the structure. Two approaches are presented in this study to solve the joint structural system parameter and input identification problem: (a) a sequential maximum likelihood estimation approach, which reduces to a sequential nonlinear constrained optimization method, and (b) a sequential maximum a posteriori estimation approach, which reduces to a sequential iterative extended Kalman filtering method. Both approaches require the computation of FE response sensitivities with respect to the unknown FE model parameters and the values of base acceleration at each time step. The FE response sensitivities are computed efficiently using the direct differentiation method. The two proposed approaches are validated using the seismic response of a 5-story reinforced concrete building structure, numerically simulated using a state-of-the-art mechanics-based nonlinear structural FE modeling technique. The simulated absolute acceleration response time histories of 3 floors and the relative (to the base) roof displacement response time histories of the building to a bidirectional horizontal seismic excitation are polluted with artificial measurement noise. The noisy responses of the structure are then used to estimate the unknown FE model parameters characterizing the nonlinear material constitutive laws of the concrete and reinforcing steel and the (assumed) unknown time history of the ground acceleration in the longitudinal direction of the building. The same nonlinear FE model of the structure is used to simulate the structural response and to estimate the dynamic input and system parameters. Thus, modeling uncertainty is not considered in this paper. Although the validation study demonstrates the estimation accuracy of both approaches, the sequential maximum a posteriori estimation approach is shown to be significantly more efficient computationally than the sequential maximum likelihood estimation approach. Copyright © 2018 John Wiley & Sons, Ltd

A Nonlinear Model Inversion Method for Joint System Parameter, Noise, and Input Identification of Civil Structures

This paper presents a framework for nonlinear system identification of civil structures using sparsely measured dynamic output response of the structure. Using a sequential maximum likelihood estimation (MLE) approach, the unknown FE model parameters, the measurement noise variances, and the input ground acceleration time histories are estimated jointly. This approach requires the computation of FE response sensitivities with respect to the unknown FE model parameters (i.e., FE parameter sensitivities) as well as the FE response sensitivities with respect to the values of the input ground acceleration at every time step (i.e., FE input sensitivities). The FE parameter and input sensitivities are computed using the direct differentiation method (DDM). The presented output-only nonlinear FE model updating method is validated using the numerically simulated seismic response of a realistic three-dimensional five-story reinforced concrete building structure. The simulated building responses to a horizontal bi-directional seismic excitation is contaminated with artificial measurement noise and used to estimate the unknown FE model parameters characterizing the nonlinear material constitutive laws of the reinforced concrete, as well as the root mean square of the measurement noise at each measurement channel, and the full time history of the seismic base acceleration. The method presented in this paper provides a powerful framework for structural system and damage identification of civil structures, when the input excitations are not measured, are partially measured, or the measured input excitations are erroneous. © 2017 The Authors. Published by Elsevier Ltd