Bioinspired Electro‐Thermo‐Hygro Reversible Shape‐Changing Materials by 4D Printing

Hygromorph composites are moisture‐induced shape‐changing materials that are increasingly studied to develop autonomously actuated deployable structures. The morphing mechanism is based on the high affinity for moisture and the hygroexpansive nature of at least one component, combined with a bilayer microstructure. Among available hygromorphs, those consisting of cellulosic or hydrogel material‐based actuators trigger fast responses to moisture. Their stiffness however decreases significantly with the moisture content and that restricts their potential application as soft actuators. This work proposes a novel 4D printed multistimuli‐responsive structural material based on conductive carbon reinforcements and combined with a moisture sensitive polymer. These 4D printed materials possess a microstructure that provides the capability of natural actuators like pine cones. The actuation of these functional materials could be either triggered passively by the variation of the ambient moisture, or by electroheating, with the latter leading to the control of the moisture content in initially wet samples via Joule effects. This new class of functional materials shows an increase of the actuation speed by a factor 10 compared to other existing hygromorphs with the same responsiveness. When the electrical heating is turned off, passive cooling and moisture driven actuation is triggered in a full reversible mode.     

High speed milling, electro discharge machining and direct metal laser sintering: A method to optimize these processes in hybrid rapid tooling

Prototype injection moulds for plastic parts must face two constraints: be designed and manufactured as quickly as possible and have a short lead time. Moreover, moulds have to evolve in the same way as the part does, to provide either a new functionality or a variant of this part. The current approach is based on a multi component tooling (hybrid rapid tooling) in order to more easily manufacture each component of the mould and to have a greater reactivity to each product evolution. In this paper, we propose a method to manufacture the mould in multi components. This approach is based on process capability criteria (i.e. topological and geometrical criteria). An industrial example is presented. We will focus on the choice between three processes mainly used in hybrid rapid tooling: high speed machining (HSM), electro discharging machining (EDM) and direct metal laser sintering (DMLS).     

Sensor fault detection and isolation filter for polytopic LPV systems: A winding machine application

In this paper, a fault diagnosis method is developed for a particular class of nonlinear systems described by a polytopic linear parameter varying (LPV) formulation. The main contribution consists in the synthesis of an accurate fault detection and isolation (FDI) filter and also a sensor fault magnitude estimation with a quality factor. This quality factor of the filter underlines if the fault estimation can be used or not. Stability conditions of the polytopic LPV filter are studied by ensuring poly-quadratic stability with Linear Matrix Inequality (LMI) representation. The effectiveness of this global FDI scheme through LPV modelization, filter design and stability analysis, is illustrated on a real winding machine under multiple sensor faults.     

Probability of Exceedance Estimates for Scour Depth around Bridge Piers

Scour at a bridge pier is the formation of a hole around the pier due to the erosion of soil by flowing water; this hole in the soil reduces the carrying capacity of the foundation and the pier. Excessive scour can cause a bridge pier to fail without warning. Current predictions of the depth of the scour hole around a bridge pier are based on deterministic models. This paper considers two alternative deterministic models to predict scour depth. For each deterministic model, a corresponding probabilistic model is constructed using a Bayesian statistical approach and available field and experimental data. The developed probabilistic models account for the estimated bias in the deterministic models and for the model uncertainty. Parameters from both prediction models are compared to determine their accuracy. The developed probabilistic models are used to estimate the probability of exceedance of scour depth around bridge piers. The method is demonstrated on an example bridge pier. The paper addresses model uncertainties for given hydrologic variables. Hydrologic uncertainties have been presented in a separate paper.     

An efficient numerical model for liquid water uptake in porous material and its parameter estimation

The goal of this study is to propose an efficient numerical model for the predictions of capillary adsorption phenomena in a porous material. The Scharfetter–Gummel numerical scheme is proposed to solve an advection–diffusion equation with gravity flux. Its advantages such as accuracy, relaxed stability condition, and reduced computational cost are discussed along with the study of linear and nonlinear cases. The reliability of the numerical model is evaluated by comparing the numerical predictions with experimental observations of liquid uptake in bricks. A parameter estimation problem is solved to adjust the uncertain coefficients of moisture diffusivity and hydraulic conductivity.     

Fragility Increment Functions for Deteriorating Reinforced Concrete Bridge Columns

The increased deformation and shear fragilities of corroding RC bridge columns subject to seismic excitations are modeled as functions of time using fragility increment functions. These functions can be applied to various environmental and material conditions by means of controlling parameters that correspond to the specific condition. For each mode of failure, the fragility of a deteriorated column at any given time is obtained by simply multiplying the initial fragility of the pristine/nondeteriorated column by the corresponding function developed in this paper. The developed increment functions account for the effects of the time-dependent uncertainties that are present in the corrosion model as well as in the structural capacity models. The proposed formulation is a useful tool for engineering practice because the fragility of deteriorated columns is obtained without any extra reliability analysis once the fragility of the pristine column is known. The fragility increment functions are expressed as functions of time t and a given deformation or shear demand. Unknown parameters involved in the models are estimated using a Bayesian updating framework. A model selection is conducted during the assessment of the unknown parameters using the Akaike information criterion and the Bayesian information criterion. For the estimation of the parameters, a set of data are obtained by first-order reliability method analysis using existing probabilistic capacity models for corroding RC bridge columns. Example fragilities of a deteriorated bridge column typical of current California’s practice are presented to demonstrate the developed methodology. The increment functions suggested in this paper can be used to assess the time-variant fragility for application to life cycle cost analysis and risk analysis.     

A Probabilistic Framework for Bayesian Adaptive Forecasting of Project Progress

Abstract:   A methodology to forecast project progress and final time-to-completion is developed. An adaptive Bayesian updating method is used to assess the unknown model parameters based on recorded data and pertinent prior information. Recorded data can include equality, upper bound, and lower bound data. The proposed approach properly accounts for all the prevailing uncertainties, including model errors arising from an inaccurate model form or missing variables, measurement errors, statistical uncertainty, and volitional uncertainty .
As an illustration of the proposed approach, the project progress and final time-to-completion of an example project are forecasted. For this illustration construction of civilian nuclear power plants in the United States is considered. This application considers two cases (1) no information is available prior to observing the actual progress data of a specified plant and (2) the construction progress of eight other nuclear power plants is available. The example shows that an informative prior is important to make accurate predictions when only a few records are available. This is also the time when forecasts are most valuable to the project manager. Having or not having prior information does not have any practical effect on the forecast when progress on a significant portion of the project has been recorded .     

Decomposition of sparse graphs into two forests, one having bounded maximum degree

Let G be a graph. The maximum average degree of G, written Mad(G), is the largest average degree among the subgraphs of G. It was proved in Montassier et al. (2010) [11] that, for any integer k?0, every simple graph with maximum average degree less than admits an edge-partition into a forest and a subgraph with maximum degree at most k; furthermore, when k?3 both subgraphs can be required to be forests. In this note, we extend this result proving that, for k=4,5, every simple graph with maximum average degree less than mk admits an edge-partition into two forests, one having maximum degree at most k (i.e. every graph with maximum average degree less than (resp. ) admits an edge-partition into two forests, one having maximum degree at most 4 (resp. 5)).