Minimum Design Loads for Buildings and Other Structures provides requirements for general structural design and includes means for determining dead, live, soil, flood, wind, snow, rain, atmospheric ice, and earthquake loads, as well as their combinations, which are suitable for inclusion in building codes and other documents. This Standard, a revision of ASCE/SEI 7-05, offers a complete update and reorganization of the wind load provisions, expanding them from one chapter into six. The Standard contains new ultimate event wind maps with corresponding reductions in load factors, so that the loads are not affected, and updates the seismic loads with new risk-targeted seismic maps. The snow, live, and atmospheric icing provisions are updated as well. In addition, the Standard includes a detailed Commentary with explanatory and supplementary information designed to assist building code committees and regulatory authorities. Standard ASCE/SEI 7 is an integral part of building codes in the United States. Many of the load provisions are substantially adopted by reference in the International Building Code and the NFPA 5000 Building Construction and Safety Code. Structural engineers, architects, and those engaged in preparing and administering local building codes will find this Standard an essential reference in their practice. Note: New orders are fulfilled from the second printing, which incorporates the errata to the first printing.

Minimum Design Loads for Buildings and Other Structures

Wind speeds over the world’s oceans have increased over the past two decades, as have wave heights. Studies of climate change typically consider measurements or predictions of temperature over extended periods of time. Climate, however, is much more than temperature. Over the oceans, changes in wind speed and the surface gravity waves generated by such winds play an important role. We used a 23-year database of calibrated and validated satellite altimeter measurements to investigate global changes in oceanic wind speed and wave height over this period. We find a general global trend of increasing values of wind speed and, to a lesser degree, wave height, over this period. The rate of increase is greater for extreme events as compared to the mean condition.

https://researchbank.swinburne.edu.au/file/bca43746-6f16-4ad2-a4b7-27f8d8d60fed/1/PDF (Published version).pdf

Global trends in wind speed and wave height over the past 25 years

Metallic biomaterials are used in medical devices in humans more than any other family of materials. The corrosion resistance of an implant material affects its functionality and durability and is a prime factor governing biocompatibility. The fundamental paradigm of metallic biomaterials, except biodegradable metals, has been “the more corrosion resistant, the more biocompatible.” The body environment is harsh and raises several challenges with respect to corrosion control. In this invited review paper, the body environment is analysed in detail and the possible effects of the corrosion of different biomaterials on biocompatibility are discussed. Then, the kinetics of corrosion, passivity, its breakdown and regeneration in vivo are conferred. Next, the mostly used metallic biomaterials and their corrosion performance are reviewed. These biomaterials include stainless steels, cobalt-chromium alloys, titanium and its alloys, Nitinol shape memory alloy, dental amalgams, gold, metallic glasses and biodegradable metals. Then, the principles of implant failure, retrieval and failure analysis are highlighted, followed by description of the most common corrosion processes in vivo. Finally, approaches to control the corrosion of metallic biomaterials are highlighted.

Corrosion of Metallic Biomaterials: A Review

Piezoelectric coefficients are constrained by the intrinsic crystal structure of the constituent material. Here we describe design and manufacturing routes to previously inaccessible classes of piezoelectric materials that have arbitrary piezoelectric coefficient tensors. Our scheme is based on the manipulation of electric displacement maps from families of structural cell patterns. We implement our designs by additively manufacturing free-form, perovskite-based piezoelectric nanocomposites with complex three-dimensional architectures. The resulting voltage response of the activated piezoelectric metamaterials at a given mode can be selectively suppressed, reversed or enhanced with applied stress. Additionally, these electromechanical metamaterials achieve high specific piezoelectric constants and tailorable flexibility using only a fraction of their parent materials. This strategy may be applied to create the next generation of intelligent infrastructure, able to perform a variety of structural and functional tasks, including simultaneous impact absorption and monitoring, three-dimensional pressure mapping and directionality detection. Piezoelectrics convert force into electrical charge, and vice versa, but the coefficients that determine piezoelectric behaviour are constrained by crystal structure. Here, metamaterials are 3D printed that show arbitrary piezoelectric coefficients.

Three-dimensional printing of piezoelectric materials with designed anisotropy and directional response.

Cyclic performance of extended end-plate connections equipped with shape memory alloy bolts

This paper proposes an improved theoretical prediction equation for Concrete-filled steel tubes (CFT) subjected to compressive forces. This ultimate load capacity is inferred from a database of 344 experimental results reported in the literature by using Gene expression programming (GEP). Moreover, a series of structural comparisons between design provisions, other mechanically-derived expressions and the proposed prediction are addressed. The levels of accuracy, practical use and phenomenological understanding of the phenomenon are pinpointed. The results obtained are in good agreement with both the experimental and theoretical predictions. Advantages and disadvantages of such type of predictions are pinpointed.

/pdf/improved-predictive-model-to-the-cross-sectional-resistance-40f10v2c12.pdf

Improved predictive model to the cross-sectional resistance of CFT

Rainfall-runoff (R-R) modelling is used to study the runoff generation of a catchment. The quantity or rate of change measure of the hydrological variable, called runoff, is important for environmental scientists to accomplish water-related planning and design. This paper proposes (i) an integrated model namely EO-ELM (an integration of equilibrium optimizer (EO) and extreme learning machine (ELM)) and (ii) a deep neural network (DNN) for one day-ahead R-R modelling. The proposed R-R models are validated at two different benchmark stations of the catchments, namely river Teifi at Glanteifi and river Fal at Tregony in the UK. Firstly, a partial autocorrelation function (PACF) is used for optimal number of lag inputs to deploy the proposed models. Six other well-known machine learning models, called ELM, kernel ELM (KELM), and particle swarm optimization-based ELM (PSO-ELM), support vector regression (SVR), artificial neural network (ANN) and gradient boosting machine (GBM) are utilized to validate the two proposed models in terms of prediction efficiency. Furthermore, to increase the performance of the proposed models, paper utilizes a discrete wavelet-based data pre-processing technique is applied in rainfall and runoff data. The performance of wavelet-based EO-ELM and DNN are compared with wavelet-based ELM (WELM), KELM (WKELM), PSO-ELM (WPSO-ELM), SVR (WSVR), ANN (WANN) and GBM (WGBM). An uncertainty analysis and two-tailed t-test are carried out to ensure the trustworthiness and efficacy of the proposed models. The experimental results for two different time series datasets show that the EO-ELM performs better in an optimal number of lags than the others. In the case of wavelet-based daily R-R modelling, proposed models performed better and showed robustness compared to other models used. Therefore, this paper shows the efficient applicability of EO-ELM and DNN in R-R modelling that may be used in the hydrological modelling field.

Data-Driven Approach for Rainfall-Runoff Modelling Using Equilibrium Optimizer Coupled Extreme Learning Machine and Deep Neural Network

Estimating optimum parameters of a new SMA damper under different earthquake ground motions

Superelastic shape memory alloys (SMAs) are unique metallic materials that undergo substantial plastic deformations and recover their original conditions when stresses are only removed without any heat treatment. SMAs have currently become prevalent for application in structural engineering because this superelastic property contributes to entire construction system by mitigating the problem of permanent deformation. Notwithstanding many structural advantages, there exist relatively few investigations on the numerical modeling of these smart materials, which had been mostly used for nonlinear analyses. For this reason, this study mainly focuses on a one-dimensional (1D) constitutive model able to simulate the inherent behavior of superelastic SMAs, taking into account phase transformation between austenite and martensite. After discussing a possible approach for the solution scheme, numerical simulation results are compared to experimental data obtained from pull-out tests that are performed on SMA bars in order to validate the adequacy for the 1D constitutive material model presented. Furthermore, the user material model based on the solution algorithm of reproducing this superelastic behavior is applied to the structural analysis with a view to assure adequacy in practical use.

Numerical simulation for the behavior of superelastic shape memory alloys

Evaporation estimation is very essential for planning and development of water resources. The study investigates the ability of new method, dynamic evolving neural-fuzzy inference system (DENFIS), in modeling monthly pan evaporation. Monthly maximum and minimum temperatures, solar radiation, wind speed, and relative humidity data obtained from two stations located in Turkey are used as inputs to the models. The results of DENFIS method were compared with the classical adaptive neural-fuzzy inference system (ANFIS) by using root mean square error (RMSE), mean absolute relative error (MARE), and Nash-Sutcliffe Coefficient (NS) statistics. Cross validation was applied for better comparison of the models. The results indicated that DENFIS models increased the accuracy of ANFIS models to some extent. RMSE, MARE, and NS of the ANFIS model were increased by 11.13, 11.45, and 6.83% for the Antalya station and 20.11, 12.94%, and 8.29% for the Antakya station using DENFIS.

/pdf/a-new-method-for-evaporation-modeling-dynamic-evolving-293kml8mnj.pdf

Jong Wan Hu

Papers

Improved predictive model to the cross-sectional resistance of CFT

Data-Driven Approach for Rainfall-Runoff Modelling Using Equilibrium Optimizer Coupled Extreme Learning Machine and Deep Neural Network

Estimating optimum parameters of a new SMA damper under different earthquake ground motions

Numerical simulation for the behavior of superelastic shape memory alloys

A New Method for Evaporation Modeling: Dynamic Evolving Neural-Fuzzy Inference System