Parametric Estimation of Wave Dispersion for System Identification of Building Structures

The linear-elastic response of a building structure subjected to an earthquake base excitation can be approximated as the response of a continuous, spatially inhomogenous, dispersive, viscoelastic solid subjected to vertically incident plane shear waves. The frequency-dependent phase velocity and attenuation of seismic energy at different wavelengths, together with the inertial properties of the multilayer solid characterize the response of the building structure. The objective of this study is to identify the structural system by estimating the parameters that characterize the propagation of seismic waves in an equivalent multilayer viscoelastic solid. To pursue this objective, first, the measured dynamic responses of a building structure are used to derive the frequency response functions (FRFs) of the floor absolute acceleration with respect to the base excitation using a seismic interferometry approach. The FRFs obtained from the measured structural responses are then compared with the FRFs estimated using analytical models for one-dimensional shear wave propagation in a multilayer Kelvin-Voigt dispersive medium. Through a recursive Bayesian estimation approach, the parameters characterizing the phase velocity and damping ratio of the multilayer medium are estimated. This study provides a step forward in seismic interferometric identification of building structures by proposing a new method for parametric estimation of shear wave velocity and damping dispersion at the story level of a building structure. The estimated shear wave velocities before and after a damage-inducing event can be used to identify permanent loss of effective lateral stiffness of the building structure at the story level, thus can provide an alternative method for structural health monitoring and damage identification

Optimization of Irrigation and Leaching Depths Considering the Cost of Water Using WASH_1D/2D Models

Optimization of water use with consideration of salinity control is a crucial task for crop production. A new scheme, “optimized irrigation”, was recently presented to determine irrigation depth using WASH_1D/2D which are numerical simulation models of water flow and solute transport in soils and crop growth. In the scheme, irrigation depth is determined such that net income is maximized considering the price of water and weather forecasts. To evaluate whether the optimized irrigation is also able to restrict salinity stress and avoid salinization without any intentional leaching, we carried out a numerical experiment for winter wheat grown in northern Sudan under the following scenarios: (1) Available water in the root zone is refilled using freshwater (0.17 g/L of NaCl) at every five days; (2) available water in the root zone is refilled using saline water (1.7 g/L) at every five days; (3) optimized irrigation using fresh water at 7-days interval; (4) optimized irrigation on a weekly basis using saline water; and (5) same as scenario 2, except for leaching is carried out at the middle of the growing season and leaching depth is optimized such that net income is maximized. The results showed that the optimized irrigation scheme automatically instructs additional water required for leaching at each irrigation event and maximizes the net income even under saline conditions

A variational Bayesian inference technique for model updating of structural systems with unknown noise statistics

Dynamic models of structural and mechanical systems can be updated to match the measured data through a Bayesian inference process. However, the performance of classical (non-adaptive) Bayesian model updating approaches decreases significantly when the pre-assumed statistical characteristics of the model prediction error are violated. To overcome this issue, this paper presents an adaptive recursive variational Bayesian approach to estimate the statistical characteristics of the prediction error jointly with the unknown model parameters. This approach improves the accuracy and robustness of model updating by including the estimation of model prediction error. The performance of this approach is demonstrated using numerically simulated data obtained from a structural frame with material non-linearity under earthquake excitation. Results show that in the presence of non-stationary noise/error, the non-adaptive approach fails to estimate unknown model parameters, whereas the proposed approach can accurately estimate them

Growth, Yield, and Water Productivity Responses of Pepper to Sub-Irrigated Planter Systems in a Greenhouse

A sub-irrigated planter (SIP) is a container irrigation technique in which water is supplied to the crop from the bottom, stored in a saturated media-filled reservoir beneath an unsaturated soil, and then delivered by capillary action to the root zone. The aim of this study was to optimize the water management and to assess the performance of this technique in terms of water use efficiency, soil moisture, and solute distribution in comparison with surface irrigation in a Mediterranean greenhouse. The experiment consisted of four SIP treatments, with a constant water level in the bottom reservoir in order to evaluate the effect of two different irrigation salinities (1.2 and 2.2 dS m−1) and two depths of substrate profiles (25 and 15 cm). The results showed that SIP is capable of significantly improving both water-use efficiency and plant productivity compared with surface irrigation. Also, a 24% average reduction in water consumption was observed while using SIP. Moreover, SIPs with a higher depth were recommended as the optimum treatments within SIPs. The type of irrigation method affected the salinity distribution in the substrate profile; the highest salinity levels were registered at the top layers in SIPs, whereas the maximum salinity levels for the surface treatments were observed at the bottom layers. SIPs provide a practical solution for the irrigation of plants in areas facing water quality and scarcity problems

Optimum management of furrow fertigation to maximize water and fertilizer application efficiency and uniformity

25 Pags.- 5 Tabls.- 5 Figs. The definitive version is available at: http://jast.modares.ac.ir/High efficiency and uniformity of water and fertilizer application are usually, considered as the ultimate goals of an appropriate design and management of irrigation and fertigation systems. The objective followed in this paper was to present a simulation-optimization model for alternate vs. conventional furrow fertigation. Two simulation models (surface fertigation and SWMS-2D models) along with an optimization approach (genetic algorithm) were employed. Inflow discharge, irrigation cutoff and start times as well as duration of fertilizer injection were chosen as decision variables to be optimized for maximizing two objective (fitness) functions based on water and nitrate application efficiency plus uniformity. Experiments were conducted to collect field data (soil water content, soil nitrate concentration, discharge and nitrate concentration in runoff, as well as advance and recession times) in order to calibrate the simulation models. The simulation-optimization model indicated that variable and fixed alternate furrow fertigations benefited from higher water and nitrate efficiencies than the conventional furrow fertigation. However, minor differences were observed between these types of furrow irrigation regarding water and nitrate uniformity. This approach substantially improved water and nitrate application efficiency as well as uniformity, taking into account the field experimental conditions. Water and nitrate application efficiencies ranged from 72 to 88% and from 70 to 89%, respectively. Christiansen uniformity coefficients for water and nitrate varied between 80 and 90% and from 86 to 96%, respectively. A higher improvement was observed in conventional furrow fertigation than those in both alternate furrow fertigation treatments. The potential of the simulation-optimization model to improve design and management of furrow fertigation is highlighted.Peer reviewe

NONLINEAR FINITE ELEMENT MODEL UPDATING FOR NONLINEAR SYSTEM AND DAMAGE IDENTIFICATION OF CIVIL STRUCTURES

Structural health monitoring (SHM) is defined as the capability to monitor the performance behavior of civil infrastructure systems as well as to detect, localize, and quantify damage in these systems. SHM technologies contribute to enhance the resilience of civil infrastructures, which are vulnerable to structural aging, degradation, and deterioration and to extreme events due to natural and man-made hazards. Given the limited financial resources available to renovate or replace them, it is crucial to implement SHM methodologies, which can help detect safety threats at an early stage, evaluate the operational risk of the infrastructure after a catastrophic event, and prioritize the urgency of the repair/retrofit or replacement of these structures.This research focuses on the development of a novel framework for nonlinear structural system identification. This framework consists of updating mechanics-based nonlinear finite element (FE) structural models using Bayesian inference methods. Recognizing structural damage as the manifestation of structural material nonlinearity, the developed framework provides a new methodology for post-disaster SHM and DID of real-world civil structures.This research is subdivided in two parts. The first part investigates the accuracy of state-of-the-art nonlinear FE modeling in predicting the cyclic and dynamic inelastic response behavior of reinforced concrete structural components and systems. Sources of inaccuracy and uncertainty in the FE modeling and simulation approach are investigated by comparing the FE-predicted structural response with high-fidelity experimental results. In the second part of this research, two frameworks for nonlinear FE model updating are proposed, developed, and validated using numerically simulated data. In the proposed frameworks, different Bayesian estimation methods are utilized to update the nonlinear FE model of a civil structure using the recorded input excitation and response of the structure during a damage-inducing earthquake event. The initial frameworks are then extended to output-only nonlinear structural system and damage identification methods. This extension not only overcomes the shortcomings of the initial frameworks in handling unmeasured or noisy input measurements, but also paves the way to a general approach to account for model uncertainties. Finally, a new information-theoretic approach is developed for the purposes of nonlinear FE model identifiability, experimental design, and optimal sensor placement