Asymmetric one‐storey elastic systems with non‐linear viscous and viscoelastic dampers: Simplified analysis and supplemental damping system design

Investigated is the accuracy in estimating the response of asymmetric one‐storey systems with non‐linear viscoelastic (VE) dampers by analysing the corresponding linear viscous system wherein all non‐linear VE dampers are replaced by their energy‐equivalent linear viscous dampers. The response of the corresponding linear viscous system is determined by response history analysis (RHA) and by response spectrum analysis (RSA) extended for non‐classically damped systems. The flexible and stiff edge deformations and plan rotation of the corresponding linear viscous system determined by the extended RSA procedure is shown to be sufficiently accurate for design applications with errors generally between 10 and 20%. Although similar accuracy is also shown for the ‘pseudo‐velocity’ of non‐linear VE dampers, the peak force of the non‐linear VE damper cannot be estimated directly from the peak damper force of the corresponding linear viscous system. A simple correction for damper force is proposed and shown to be accurate (with errors not exceeding 15%). For practical applications, an iterative linear analysis procedure is developed for determining the amplitude‐ and frequency‐dependent supplemental damping properties of the corresponding linear viscous system and for estimating the response of asymmetric one‐storey systems with non‐linear VE dampers from the earthquake design (or response) spectrum. Finally, a procedure is developed for designing non‐linear supplemental damping systems that satisfy given design criteria for a given design spectrum. Copyright © 2003 John Wiley & Sons, Ltd.     

Inelastic displacement ratios for evaluation of existing structures

Results of a detailed statistical study of constant relative strength inelastic displacement ratios to estimate maximum lateral inelastic displacement demands on existing structures from maximum lateral elastic displacement demands are presented. These ratios were computed for single‐degree‐of‐freedom systems with different levels of lateral strength normalized to the strength required to remain elastic when subjected to a relatively large ensemble of recorded earthquake ground motions. Three groups of soil conditions with shear wave velocities higher than 180m/s are considered. The influence of period of vibration, level of lateral yielding strength, site conditions, earthquake magnitude, distance to the source, and strain‐hardening ratio are evaluated and discussed. Mean inelastic displacement ratios and those associated with various percentiles are presented. A special emphasis is given to the dispersion of these ratios. It is concluded that distance to the source has a negligible influence on constant relative strength inelastic displacement ratios. However, for periods smaller than 1s earthquake magnitude and soil conditions have a moderate influence on these ratios. Strain hardening decreases maximum inelastic displacement at a fairly constant rate depending on the level of relative strength for periods of vibration longer than about 1.0s while it decreases maximum inelastic displacement non‐linearly as the period of vibration shortens and as the relative‐strength ratio increases for periods of vibration shorter than 1.0s. Finally, results from non‐linear regression analyses are presented that provide a simplified expression to be used to approximate mean inelastic displacement ratios during the evaluation of existing structures built on firm sites. Copyright © 2003 John Wiley & Sons, Ltd.     

Landslide susceptibility analysis using GIS and artificial neural network

The purpose of this study is to develop landslide susceptibility analysis techniques using an arti?cial neural network and to apply the newly developed techniques to the study area of Yongin in Korea. Landslide locations were identi?ed in the study area from interpretation of aerial photographs, ?eld survey data, and a spatial database of the topography, soil type and timber cover. The landslide‐related factors (slope, curvature, soil texture, soil drainage, soil effective thickness, timber age, and timber diameter) were extracted from the spatial database. Using those factors, landslide susceptibility was analysed by arti?cial neural network methods. The landslide susceptibility index was calculated by the back‐propagation method, which is a type of arti?cial neural network method, and the susceptibility map was made with a geographic information system (GIS) program. The results of the landslide susceptibility analysis were veri?ed using landslide location data. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide location. A GIS was used to ef?ciently analyse the vast amount of data, and an arti?cial neural network to be an effective tool to maintain precision and accuracy. The results can be used to reduce hazards associated with landslides and to plan land use and construction. Copyright © 2003 John Wiley & Sons, Ltd.     

System identification of the Vincent Thomas suspension bridge using earthquake records

The Vincent Thomas Bridge in the Los Angeles metropolitan area, is a critical artery for commercial traffic flow in and out of the Los Angeles Harbor, and is at risk in the seismically active Southern California region, particularly because it straddles the Palos Verdes fault zone. A combination of linear and non‐linear system identification techniques is employed to obtain a complete reduced‐order, multi‐input–multi‐output (MIMO) dynamic model of the Vincent Thomas Bridge based on the dynamic response of the structure to the 1987 Whittier and 1994 Northridge earthquakes. Starting with the available acceleration measurements (which consists of 15 accelerometers on the bridge structure and 10 accelerometers at various locations on its base), an efficient least‐squares‐based time‐domain identification procedure is applied to the data set to develop a reduced‐order, equivalent linear, multi‐degree‐of‐freedom model. Although not the main focus of this study, the linear system identification method is also combined with a non‐parametric identification technique, to generate a reduced‐order non‐linear mathematical model suitable for use in subsequent studies to predict, with good fidelity, the total response of the bridge under arbitrary dynamic environments. Results of this study yield measurements of the equivalent linear modal properties (frequencies, mode shapes and non‐proportional damping) as well as quantitative measures of the extent and nature of non‐linear interaction forces arising from strong ground shaking. It is shown that, for the particular subset of observations used in the identification procedure, the apparent non‐linearities in the system restoring forces are quite significant, and they contribute substantially to the improved fidelity of the model. Also shown is the potential of the identification technique under discussion to detect slight changes in the structure's influence coefficients, which may be indicators of damage and degradation in the structure being monitored. Difficulties associated with accurately estimating damping for lightly damped long‐span structures from their earthquake response are discussed. The technical issues raised in this paper indicate the need for added spatial resolution in sensor instrumentation to obtain identified mathematical models of structural systems with the broadest range of validity. Copyright © 2003 John Wiley & Sons, Ltd.     

Surface analysis of pyrite in the No. 9 coal seam, Wuda Coalfield, Inner Mongolia, China, using high-resolution time-of-flight secondary ion mass-spectrometry

The chemical composition of pyrite in coal can be used to investigate its geological and mineralogical origin. In this paper, high-resolution time-of-flight secondary ion mass spectrometry (TOF-SIMS) was used to study the chemical composition of various pyrite forms in the No. 9 coal seam (S_t,d=3.46%) from the Wuda Coalfield, Inner Mongolia, northern China. These include bacteriogenic, framboidal, massive, cell-filling, fracture-filling, and nodular pyrites. In addition to Fe⁺ (⁵⁴Fe⁺, ⁵⁶Fe⁺, ⁵⁷Fe⁺), other fragment ions were detected in bacteriogenic pyrites, such as ²⁷Al⁺, Si⁺ (²⁸Si⁺, ²⁹Si⁺, ³⁰Si⁺), ⁴⁰Ca⁺, Cu⁺ (⁶³Cu⁺, ⁶⁵Cu⁺), Zn⁺ (⁶⁴Zn⁺, ⁶⁶Zn⁺, ⁶⁷Zn⁺, ⁶⁸Zn⁺), Ni⁺ (⁵⁸Ni⁺, ⁶⁰Ni⁺, ⁶²Ni⁺), and C₃H₇⁺. TOF-SIMS images show bacteriogenic pyrites are relatively rich in Cu, Zn, and Ni, suggesting that bacteria probably play an important role in the enrichment of Cu, Zn, and Ni during their formation. Intense positive secondary ion fragments from framboidal aggregates, such as ²⁷Al⁺, ²⁸Si⁺, ²⁹Si⁺, AlO⁺, CH₂⁺, C₃H₃⁺, C₃H₅⁺, and C₄H₇⁺, indicate that formation of the framboidal aggregates may have occurred together with clay mineral and organic matter, which probably serve as the binding substance. The intense ions of ²⁸Si⁺ and ²⁷Al⁺ from massive pyrites also suggest that their pores incorporated clay minerals during crystallization. Together with the lowest ²⁸Si⁺/²³Na⁺ value, the intense organic positive secondary ion peaks from cell-filling pyrites, such as C₃H₃⁺, C₃H₅⁺, C₃H₇⁺, and C₄H₇⁺, indicate that pyrite formation may have accompanied dissolution or disintegration of the cell. The intense P⁺ peak was observed only in the fracture-filling pyrite and the highest ²⁸Si⁺/²³Na⁺ value of fracture-filling pyrite reflects its epigenetic origin. Together with XRD and REEs data, the stronger ⁴⁰Ca⁺ in nodular pyrite than other pyrite forms shows seawater influence during its formation.     

Vanadium in peridotites as a proxy for paleo-fO2 during partial melting : prospects, limitations, and implications

The compatibility of vanadium (V) during mantle melting is a function of oxygen fugacity (fO₂): at high fO₂’s, V becomes more incompatible. The prospects and limitations of using the V content of peridotites as a proxy for paleo-fO₂ at the time of melt extraction were investigated here by assessing the uncertainties in V measurements and the sensitivity of V as a function of degree of melt extracted and fO₂. V-MgO and V-Al₂O₃ systematics were found to be sensitive to fO₂ variations, but consideration of the uncertainties in measurements and model parameters indicates that V is sensitive only to relative fO₂ differences greater than ∼2 log units. Post-Archean oceanic mantle peridotites, as represented by abyssal peridotites and obducted massif peridotites, have V-MgO and -Al₂O₃ systematics that can be modeled by 1.5 GPa melting between FMQ − 3 and FMQ − 1. This is consistent with fO₂’s of the mantle source for mid-ocean ridge basalts (MORBs) as determined by the Fe³⁺ activity of peridotitic minerals and basaltic glasses. Some arc-related peridotites have slightly lower V for a given degree of melting than oceanic mantle peridotites, and can be modeled by 1.5 GPa melting at fO₂’s as high as FMQ. However, the majority of arc-related peridotites have V-MgO systematics overlapping that of oceanic mantle peridotites, suggesting that although some arc mantle may melt under slightly oxidizing conditions, most arc mantle does not. The fact that thermobarometrically determined fO₂’s in arc peridotites and lavas can be significantly higher than that inferred from V systematics, suggests that V retains a record of the fO₂ during partial melting, whereas the activity of Fe³⁺ in arc peridotitic minerals and lavas reflect subsequent metasomatic overprints and magmatic differentiation/emplacement processes, respectively.Peridotites associated with middle to late Archean cratonic mantle are characterized by highly variable V-MgO systematics. Tanzanian cratonic peridotites have V systematics indistinguishable from post-Archean oceanic mantle and can be modeled by 3 GPa partial melting at ∼FMQ − 3. In contrast, many South African and Siberian cratonic peridotites have much lower V contents for a given degree of melting, suggesting at first glance that partial melting occurred at high fO₂’s. More likely, however, their unusually low V contents for a given degree of melting may be artifacts of excess orthopyroxene, a feature that pervades many South African and Siberian peridotites but not the Tanzanian peridotites. This is indicated by the fact that the V contents of South African and Siberian peridotites are correlated with increases in SiO₂ content, generating data arrays that cannot be modeled by partial melting but can instead be generated by the addition of orthopyroxene through processes unrelated to primary melt depletion. Correction for orthopyroxene addition suggests that the South African and Siberian peridotites have V-MgO systematics similar to those of Tanzanian peridotites. Thus, if the Tanzanian peridotites represent the original partial melting residues, and if the South African and Siberian peridotites have been modified by orthopyroxene addition, then there is no indication that Archean cratonic mantle formed under fO₂’s significantly greater than that of modern oceanic mantle. Instead, the fO₂’s inferred from the V systematics in these three cratonic peridotite suites are within range of modern oceanic mantle. This also suggests that the transition from a highly reducing mantle in equilibrium with a metallic core to the present oxidized state must have occurred by late Archean times.     

Computational study of tetrahedral Al–Si and octahedral Al–Mg ordering in phengite

 As part of a wider study of the nature and origins of cation order–disorder in micas, a variety of computational techniques have been used to investigate the nature of tetrahedral and octahedral ordering in phengite, K₂ ^[6](Al₃Mg)^[4](Si₇Al)O₂₀(OH)₄. Values of the atomic exchange interaction parameters J _n used to model the energies of order–disorder were calculated. Both tetrahedral Al–Si and octahedral Al–Mg ordering were studied and hence three types of interaction parameter were necessary: for T–T, O–O and T–O interactions (where T denotes tetrahedral sites and O denotes octahedral sites). Values for the T–T and O–O interactions were taken from results on other systems, whilst we calculated new values for the T–O interactions. We have demonstrated that modelling the octahedral and tetrahedral sheets alone and independently produces different results from modelling a whole T–O–T layer, hence justifying the inclusion of the T–O interactions. Simulations of a whole T–O–T layer of phengite indicated the presence of short-range order, but no long-range order was observed. Received: 8 August 2002 / Accepted: 14 February 2003 Acknowledgements The authors are grateful to EPSRC (EJP) and the Royal Society (CIS) for financial support. Monte Carlo simulations were performed on the Mineral Physics Group's Beowulf cluster and the University of Cambridge's High Performance Computing Facility.