Experimental study of crushed aggregate shape

Aggregate shape is a key parameter that influences mixtures properties. This paper accounts for a closely controlled experimental program aimed at clarifying the variation of aggregate shape with two parameters, gyratory crusher setting and feed grading. The results show that crusher setting has a significant influence on the flakiness index of produced elementary size fractions, which is not the case for spread size fractions. Moreover, a variation of the proportions of feed fraction constituents has no significant influence on the flakiness index of produced aggregates, whereas a variation of the spreading or size of the feed fraction has. Regarding the shape characteristics of particles, it has been observed that the dimensions of a particle were not related, but flat particles are elongated, and vice versa. Finally, it has been observed that flakiness index varies conversely with the size of produced aggregate.     

Fiber–fiber interactions in carbon mat thermoplastics

Carbon mat thermoplastics (CMT) consisting of 12.7 mm long, chopped carbon fibers in a polypropylene matrix were manufactured using the wetlay technique at fiber volume fractions (FVF) from 10% to 25%, and tests simulating the compression molding process were conducted. The packing stress of the CMT followed a power law relation with FVF. A single fiber pull-out fixture was used to measure the frictional and hydrodynamic lubrication coefficients at fiber–fiber touch points, and results were fit with an existing relation for glass mat thermoplastics. In isothermal squeeze flow the load–displacement behavior for the 10% FVF CMT was similar in shape to that for a fluid with a yield stress. However, for FVFs of 15–25%, the load–displacement curves showed a load spike at the beginning of the flow, then followed the curve for a fluid with a yield stress. The spike was attributed to fiber breakage that increased with increasing FVF of the sample.     

Genetic algorithm hybridized with multilayer perceptron to have an economical slope stability design

The present work aimed to evaluate and optimize the design of an artificial neural network (ANN) combined with an optimization algorithm of genetic algorithm (GA) for the calculation of slope stability safety factors (SF) in a pure cohesive slope. To make datasets of training and testing for the developed predictive models, 630 finite element limit equilibrium (FELE) analyses were performed. Similar to many artificial intelligence-based solutions, the database was involved in 189 testing datasets (e.g., 30% of the entire database) and 441 training datasets; for example, a range of 70% of the total database. Moreover, variables of multilayer perceptron (MLP) algorithm (for example, number of nodes in any hidden layer) and the algorithm of GA like population size was optimized by utilizing a series of trial and error process. The parameters in input, which were used in the analysis, consist of slope angle (β), setback distance ratio (b/B), applied stresses on the slope (F_y) and undrained shear strength of the cohesive soil (C_u) where the output was taken SF. The obtained network outputs for both datasets from MLP and GA-MLP models are evaluated according to many statistical indices. A total of 72 MLP trial and error (e.g., parameter study) the optimal architecture of 4 × 8 × 1 were determined for the MLP structure. Both proposed techniques result in a proper performance; however, according to the statistical indices, the GA–MLP model can somewhat accomplish the least mean square error (MSE) when compared to MLP. In an optimized GA–MLP network, coefficient of determination (R²) and root mean square error (RMSE) values of (0.975, and 0.097) and (0.969, and 0.107) were found, respectively, to both of the normalized training and testing datasets.

     

Fuzzy reliability analysis of nanocomposite ZnO beams using hybrid analytical-intelligent method

A hybrid analytical-intelligent approach is proposed for fuzzy reliability analysis of the composite beams reinforced by zinc oxide (ZnO) nanoparticle. The fuzzy reliability index corresponding to buckling failure mode of nanocomposite beam under thickness-direction external voltage is computed based on three-levels: (1) fuzzy analysis, (2) reliability analysis and (3) analytical buckling analysis. In fuzzy analysis level, an improved gravitational search algorithm has been applied to determine uncertainty interval for membership levels of reliability index. The adaptive formulation with a dynamical self-adjusting process is used for reliability analysis level based on conjugate first-order reliability method (FORM). The self-adjusting term in conjugate sensitivity vector is used to satisfy the sufficient descent condition for controlling instability of FORM formula while the proposed conjugate scalar factor is computed less than the original conjugate FORM, thus it may be provided with the efficient results for the convex problem. The new and previous sensitivity vectors obtained by conjugate and steepest descent vectors dynamically adjusted the proposed conjugate factor. In the buckling analysis level, an exponential theory in conjunction with the method of energy is utilized. Fuzzy random variables including applied voltage, the volume fraction of ZnO, thickness of beam, spring constant and shear constant of the foundation are considered in studied nanocomposite beam. Survey results indicated that the proposed method can provide stable and acceptable fuzzy membership functions for parametric study. Moreover, the ratio of length to thickness and spring constant of foundation are the more sensitive parameters which affect fuzzy reliability index significantly.

     

A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.

     

A novel deep learning scheme for multi-condition remaining useful life prediction of rolling element bearings

The remaining useful life (RUL) prediction of a rolling element bearing is important for more reasonable maintenance of machinery and equipment. Generally, the information of a failure can hardly be acquired in advance while running and the degradation process varies in terms of different faults. Thus, fault identification is indispensable for a multi-condition RUL prediction, where, however, the fault identification and RUL prediction are separated in most studies. A new hybrid scheme is proposed in this paper for the multi-condition RUL prediction of rolling element bearings. The proposed scheme contains both classification and regression, where the 2D-DCNN based classifier and predictors are built concerning typical fault conditions of a bearing. For the online prediction, the raw signals are spanned in the time-frequency domain and then transferred into images as the input of the scheme. The classifier is used to monitor the vibration of rolling bearings for online fault recognition and excite the corresponding predictor for RUL prediction once a fault is detected. The output from the predictor is amended by the proposed adaptive delay correction method as the final prediction results. A demonstration is performed based on the XJTU-SY datasets and the results are compared with those from the state-of-the-art methods, which proves the superiority of the proposed scheme in improving the accuracy and linearity of RUL prediction. The time cost of the proposed online prediction scheme is also investigated and the results indicate high time effectiveness.     

Public key distribution scheme for delay tolerant networks based on two-channel cryptography

A delay tolerant network (DTN) is a store carry and forward network characterized by highly mobile nodes, intermittent connectivity with frequent disruptions, limited radio range and physical obstructions. Emerging applications of DTN include rural DTN, vehicular DTN and pocket DTN. The development of DTN raises a number of security-related challenges due to inconsistent network access and unreliable end-to-end network path. One of the challenges is initial secure context establishment as it is unrealistic to assume that public key infrastructure (PKI) is always globally present and available, hence, the public key management becomes an open problem for DTN. In this paper, for the first time, we propose a dynamic virtual digraph (DVD) model for public key distribution study by extending graph theory and then present a public key distribution scheme for pocket DTN based on two-channel cryptography. By distinguishing between owners and carriers, public key exchange and authentication issues in the decentralized pocket DTN environment can be solved by a two-channel cryptography process and our simulation results have proven it.     

Automatic execution of workflows on laser-scanned data for extracting bridge surveying goals

With the capability of capturing detailed geometry of bridges in minutes, laser scanning technology has attracted the interests of bridge inspectors and researchers in the domain of bridge management. A challenge of effectively utilizing laser scanned point clouds for bridge inspection is that inspectors need to manually extract and measure large numbers of geometric features (e.g., points) for deriving geometric information items (e.g., the minimum underclearance) of bridges, named as bridge surveying goals in this research. Tedious manual data processing impedes inspectors from quantitatively understanding how various data processing options (e.g., algorithms, parameter values) influence the data processing time and the reliabilities of the surveying goal results. This paper shows the needs of automatic workflow executions for extracting surveying goals from laser scanned point clouds, and presents a computational framework for addressing these needs. This computational framework is composed of formal representations of workflows and mechanisms for constructing and executing workflows. Using a prototype system implemented based on this framework, we constructed and quantitatively characterized three workflows for extracting three representative bridge surveying goals, using three metrics of workflow performance defined in this research: exhaustiveness of measurement sampling, reliability of surveying goal results, and time efficiency.     

Flexible and robust CAD models for design automation

This paper explores novel methodologies for enabling Multidisciplinary Design Optimization (MDO) of complex engineering products. To realize MDO, Knowledge Based Engineering (KBE) is adopted with the aim of achieving design reuse and automation. The aim of the ongoing research at Linköping University is to shift from manual modeling of disposable geometries to Computer Aided Design (CAD) automation by introducing generic high level geometry templates. Instead of repeatedly modeling similar instances of objects, engineers should be able to create more general models that can represent entire classes of objects. The proposed methodology enables utilization of commercial design tools, hence taking industrial feasibility into consideration. The concept of High Level CAD templates (HLCt) will be proposed and discussed as the building blocks of flexible and robust CAD models, which in turn enables high fidelity geometry in the MDO loop. Furthermore, quantification of the terms flexibility and robustness are presented, providing a means to measure the quality of the geometry models. Finally, application examples are presented in which the outlined framework is evaluated. The applications have been chosen from three ongoing research projects aimed at automating the design of transport aircraft, industrial robots, and micro air vehicles.     

Supply chain trust diagnosis (SCTD) using inductive case-based reasoning ensemble (ICBRE): The case of general competence trust diagnosis

General competence trust among supply chain partners, referring to the trust that a partner holds the general ability of fulfilling contracts, is a critical factor to ensure effective cooperation in a supply chain, especially in the current financial crisis. The method of supply chain trust diagnosis (SCTD) is to evaluate whether or not a partner holds such competence. This research devotes to an early investigation on diagnosing competence trust of supply chain with the method of inductive case-based reasoning ensemble (ICBRE). The so-called supply chain trust diagnosis with inductive case-based reasoning ensemble consists of five levels, that is, information level, the level of ratios of general competence states, the level of inductive case-based reasoning, ensemble level, and diagnosis result level. Knowledge for diagnosing competence trust, which composes of a case base, is hidden in data represented by ratios of general competence states. Inductive approach is combined with randomness to construct diverse and good member methods of inductive case-based reasoning. Finally, simple voting is used to integrate outputs of member inductive case-based reasoning methods in order to produce the final diagnosis on whether or not a partner holds the general ability of fulfilling contracts. We statistically validated results of the method of supply chain trust diagnosis with inductive case-based reasoning ensemble by comparing them with those of multivariate discriminant analysis, logistic regression, single Euclidean case-based reasoning, and single inductive case-based reasoning. The results indicate that the method of supply chain trust diagnosis with inductive case-based reasoning ensemble significantly improves predictive capability of case-based reasoning in this problem and outperforms all the comparative models by group decision of several decision-making agents and non-strict assumptions like statistical methods.