Introduction

Strong ground shaking during the 2011 off the Pacific coast of Tohoku earthquake (the 2011 Great East Japan Earthquake) damaged pile foundations over wide areas in Miyagi and Chiba prefectures in addition to causing various other forms of foundation damage through liquefaction-induced ground deformation, slope failure and complete destruction by tsunami. Such damage to pile foundations, which has been commonly observed in past major earthquakes, resulted in building structure tilting even though superstructures remained practically intact. Pile foundation repair and careful jacking-up to level were required to restore the buildings for use. Most of the damaged buildings were constructed before the Japanese code requirements for the seismic design of foundations went into effect in 1985, while several of them were constructed after 1985. According to the damage investigation,1 74% of the damaged buildings were constructed before 1985. Since approximately 26% of the damage occurred in buildings constructed since 1985, the year of construction does not appear to be a crucial factor in the damage to piles. In addition, pile damage notably occurred in precast concrete piles known to have non-ductile deformation characteristics, and the soil in which damage was observed consisted of very soft clay, a liquefiable layer and/or non-homogeneous strata in most cases. These conditions indicate that significant effects of nonlinear behavior of piles and soil materials must be considered when simulating the pile failure process. The authors focused on a case in which pile damage resulted in building tilting although the pile foundation design was based on a simple linear elastic analysis in accordance with the existing design code in 1987. An analytical study was performed using a foundation structure model to simulate the distinctive pile failure modes observed during the 2011 off the Pacific coast of Tohoku earthquake. In the analysis model, a remarkably damaged frame was removed and used as a calculation model consisting of piles with soil springs. Based on a previous study2 on the seismic performance of pile foundations during severe earthquakes, nonlinear flexural rigidity of the pile element and rotational stiffness at the pile head were adopted. The nonlinear characteristics of the soil springs were also adjusted in accordance with soil type and depth (overburden pressure). In addition, the pile group effect was considered depending on the position and spacing of each pile in a group. The seismic response of the building and the ground deformation were evaluated based on a free field ground response analysis using ground motions recorded at an observation point near the site. The inertia force and axial force at the pile heads were estimated based on the results of elastic analysis.

The Fiji Islands in the South Pacific are highly exposed to climate-induced hazards and have experienced several flood and cyclone events in recent years. Drawing on a series of field studies in the lower Ba River Catchment on Fiji’s main island Viti Levu, the objective of this paper is to determine how climate adaptation strategies – employed by indigenous Fijian communities and households – are influenced by socio-cultural values and access to resources, information and power. Our multi-method approach has been conceptually informed by Agrawal and Perrin’s (2008) climate adaptation framework and included semi-structured interviews at the household level, and participatory hazard mapping with diverse focus groups at the community level. Our study finds that due to diverse value-based assessments of livelihood opportunities and climate-related risks, communal and household adaptive strategies can differ widely, even in a very localized cultural context. We also show how decisions to relocate from ‘risky environments’ are influenced by a combination of local power relations, attachment to cultural and social space, and the provision of external assistance. Our findings comment on the need for disaster risk reduction strategies to recognize how different groups and households respond to climate-related events in distinct socially determined ways.

Introduction

The Fiji Islands – like most tropical Pacific Island countries – are highly exposed to climate-induced hazards, such as cyclones, storm surges and floods, which are predicted to increase in frequency and intensity (Lough, Gupta, Power, Grose, & McGree, 2015; Chandra & Gaganis, 2016; Janif et al., 2016; Chand, Tory, Hua, & Walsh, 2017). Fiji’s largest island, Viti Levu, has experienced a number of flood and cyclone events in recent years, with major disasters occurring in 2009 (floods triggered by Tropical Depression 04F), 2012 (two consecutive floods caused by Tropical Disturbance 06F and Tropical Depression TD17F in January and March respectively; Tropical Cyclone Evan in December) and 2016 (Tropical Cyclone Winston, the most powerful cyclone that hit the South Pacific Islands in recorded history). The Fijian government has identified 676 communities – most of them located in coastal areas – as being at high risk of climate-related hazards and earmarked them for future relocation (Leckie, 2016). The focus on government-led relocation in the national policy discourse may reflect a lack of trust in local adaptive capacity. While relocation may be alternatively seen as adaptive strategy, failure to adapt, or measure of last resort, the need to better understand and strengthen adaptive capacity of Fijian households and communities is irrefutable. This paper builds on research carried out from 2015 to 2016 examining adaptation strategies employed at community and household levels after two major flood events in 2012 affecting downstream communities of the Ba River Catchment in Western Viti Levu, Fiji Islands. Adopting a multidisciplinary approach, the objectives of this study were twofold: (1) identify the factors that enhance the resilience and adaptive capacity of flood-affected communities, and (2) examine the extent to which adaptation practices are contingent upon socio-cultural values and access to resources, information and power.

Tidal waves are produced by changes in the gravitational forces of the sun and the moon. Prediction of tidal currents are necessitated by practical requirements like navigation, protection from flooding, coastal management to recent developments of energy extraction. Theoretical understanding of the tidal phenomenon began with Newton pioneering the gravitational theory and then later, Laplace deriving the expression for the tidal potential. There have been many advances in methodologies for tidal analysis since then. The most widely used method is that of the harmonic analysis (HA), where the observed tidal variations are considered as a resultant of various periodic components of known frequencies, with the amplitudes and phases determined using the leastsquares fitting procedure. Computer codes based on HA have been used for decades for the prediction of tidal heights (1-D) and currents (2- D). Over the years various advances have been made to HA approach [see e.g. Pawlowicz et al., 2002; Foreman et al., 2009; Leffler and Jay, 2009]. Other techniques include tidal spectroscopy (Munk and Cartwright, 1966), and response method for unified tide and surge prediction (Cartwright, 1968), however they have not been widely adopted. HA has been extensively used in the analysis of stationary tidal (height) records, providing insights into the tidal dynamics. However, there are several shortcomings of this methodology. One of the challenging tasks in HA is the selection of tidal constituents, which if inaccurate can lead to overfitting of data or numerical issues (Jay and Flinchem, 1999). Appropriate modelling of noise is another issue. In tidal analysis, signals which do not contribute to the tidal variations are classified as ‘noise. In reality, there can be cases where the non-tidal signal is much stronger than the tidal e.g. the occurrence of a stormy event, and many of such effects are non-harmonic. It is difficult to incorporate such effects in the tidal HA formulation. In general, the technique is not suitable for application to non-stationary data (Jay and Flinchem, 1999). HA is also incapable of modelling the spatial variability of tides – this is not a big issue in modelling tidal heights which changes slowly in space, however tidal currents can vary sharply within short distances due to changes in bathymetry and topography. As tides move into shallow waters, they are distorted resulting in overtides (higher harmonics of principal constituents) and compound tides (interaction between different constituents). Such interactions can lead to asymmetry in the flood and ebb magnitudes of the current, depending on the phase relationship (Friedrichs and Aubrey, 1988). In HA, nonlinear characteristics are incorporated with the inclusion of shallow water constituents, some of which may need to be inferred, and such operations are often difficult. Even more complexities can result near headlands (Geyer, 1993), where complex flow structures can result in additional frequencies, which are not necessarily sinusoidal. In relation to the uncertainty estimation, HA generates confidence intervals for the current ellipse parameters (which are often large (Leffler and Jay, 2009)). However, in a lot of practical applications it is more useful to generate confidence interval estimates directly in the time domain.

مقاله سال 2017

 مجله امگا – Omega

We study optimization models and algorithms for the problem of finding an optimal configuration for an array of wave energy converter (WEC) devices. Ocean wave energy represents a large untapped source of energy in the world and potentially offers a vast source of sustainable energy (Surrey and Page 1974, Wei et al. 2015). According to the Electric Power Research Institute (EPRI 2011), the total potential wave energy resource along the U.S. continental shelf edge is estimated to be 1,170 TWh per year, which is almost one third of the annual electricity consumption in the U.S. This energy resource has the advantage of being in close proximity to the coastal load centers in the U.S., which makes the transmission of the generated energy more efficient than, say, wind farms located in the geographical center of the country. Moreover, wave energy is more predictable and stable than wind and solar energy. For a summary of emerging WEC technologies, see, e.g., Kishore et al. (2013), Lopez and Iglesias ´ (2014).

In the present work, we study the problem of determining the optimal layout of multiple WECs in an array, known as a wave farm. A wave farm’s configuration or layout can have a significant effect on the power output of the farm, depending on the nature of the interference (constructive or destructive) among the incident ocean waves and the scattered and radiated waves produced by the WECs. In the interactions between waves and WECs or other rigid bodies, there are three types of waves (Mei 1989): Incident waves represent disturbances due to natural forces and occur whether or not the body is present; in other words, they are the incoming ocean waves. Scattered waves correspond to the collision and dispersion of incident waves with a fixed body; they represent the way in which incoming waves are deformed by the body. Finally, the motion of the body itself produces radiated waves, similar to the circular waves produced by a stone dropped into a lake. (The same three types of waves are relevant for electromagnetic waves.)

When the incident wave hits a WEC, therefore, it produces scattered and radiated waves that interact with each other and with the incident wave. Downstream WECs experience the combined wave and, in turn, produce their own scattered and radiated waves. We would like all of the WECs to be located at points of constructive interference so that they all experience waves whose amplitudes are as large as possible, thus maximizing the power produced. Unfortunately, the layout problem is complex, since the mathematical model of the hydrodynamic interactions is nonconvex and computationally expensive, and since the objective function is non separable, i.e., the power at a given WEC depends on the locations of all the other WECs.