Fuzzy logic-based prediction of compressive strength of lightweight geopolymers

In the present work, compressive strength of lightweight inorganic polymers (geopolymers) produced by fine fly ash and rice husk–bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled based on fuzzy logic. To build the model, training, validating and testing were conducted using experimental results from 144 specimens. The used data in the ANFIS models were arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing and the test trial number. According to these input parameters, in the model, the compressive strength of each specimen was predicted. The training, validating and testing results in the model have shown a strong potential for predicting the compressive strength of the geopolymer specimens in the considered range.     

Retraction Note to: Fuzzy logic-based prediction of compressive strength of lightweight geopolymers

Neural Computing and Applications - The Editor-in-Chief has retracted this article [1] because it significantly overlaps with a number of previously published articles including     

Rule-based Mamdani type fuzzy logic model for the prediction of compressive strength of silica fume included concrete using non-destructive test results

In this study, a fuzzy logic model for predicting compressive strength of concretes containing silica fume (SF) (0, 5, 10%) has been developed using non-destructive testing results [ultrasonic pulse velocity (km/s) and Schmidt hardness (R)]. Experimental results of non-destructive tests and the amount of the SF were used to construct the model. Result have shown that fuzzy logic systems have strong potential for predicting 7, 28, and 90 days compressive strength using ultrasonic pulse velocity (km/s), Schmidt hardness (R), and silica fume content (%) as inputs.     

Fuzzy logic as a logic of the expressive strength of information

We develop alternative semantics for Łukasiewicz logic and for cancellative hoop logic according to the following idea. We formalize statements reflecting an inexact knowledge of certain (sharp) properties; we assume that all what can be known about a property is its expressive strength. To this end, we consider a Boolean algebra endowed with an automorphism group or, alternatively, with a measure. The Boolean algebra is meant to model a collection of properties; and the additional structure is used to identify pairs of properties which, although possibly distinct, are equally strong. Propositions are defined as subsets of the algebra containing with any element also those identified with it in this way. We show that then, the set of all propositions carries the structure of an MV-algebra or of a cancellative hoop.     

Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete

In this study, an artificial neural network (ANN) and fuzzy logic (FL) study were developed to predict the compressive strength of silica fume concrete. A data set of a laboratory work, in which a total of 48 concretes were produced, was utilized in the ANNs and FL study. The concrete mixture parameters were four different water–cement ratios, three different cement dosages and three partial silica fume replacement ratios. Compressive strength of moist cured specimens was measured at five different ages. The obtained results with the experimental methods were compared with ANN and FL results. The results showed that ANN and FL can be alternative approaches for the predicting of compressive strength of silica fume concrete.     

Fuzzy Logic Based Group Maturity Rating for Software Performance Prediction

Driven by market requirements,software services organizations have adopted various software engineering process models (such as capability maturity model (CMM),capability maturity model integration (CMMI),ISO 9001:2000,etc.) and practice of the project management concepts defined in the project management body of knowledge.While this has definitely helped organizations to bring some methods into the software development madness,there always exists a demand for comparing various groups within the organization in terms of the practice of these defined process models.Even though there exist many metrics for comparison,considering the variety of projects in terms of technology,life cycle,etc.,finding a single metric that caters to this is a difficult task.This paper proposes a model for arriving at a rating on group maturity within the organization.Considering the linguistic or imprecise and uncertain nature of software measurements,fuzzy logic approach is used for the proposed model.Without the barriers like technology or life cycle difference,the proposed model helps the organization to compare different groups within it with reasonable precision.     

Fuzzy polynomial neural networks for approximation of the compressive strength of concrete

The main purpose of this paper is to develop fuzzy polynomial neural networks (FPNN) to predict the compressive strength of concrete. Two different architectures of FPNN are addressed (Type1 and Type2) and their training methods are discussed. In this research, the proposed FPNN is a combination of fuzzy neural networks (FNNs) and polynomial neural networks (PNNs). Here, while the FNN demonstrates the premises (If-Part) of the fuzzy model, the PNN is implemented as its consequence (Then-Part). To enhance the performance of the network, back propagation (BP), and list square error (LSE) algorithms are utilized for the tuning of the system.Six different FPNN architectures are constructed, trained, and tested using the experimental data of 458 different concrete mix-designs collected from three distinct sources. The data are organized in a format of six input parameters of concrete ingredients and one output as 28-day compressive strength of the mix-design. Using root means square (RMS) and correlation factors (CFs), the models are evaluated and compared with training and testing data pairs. The results show that FPNN-Type1 has strong potential as a feasible tool for prediction of the compressive strength of concrete mix-design. However, the FPNN-Type2 is recognized as unfeasible model to this purpose.     

Fuzzy logic model of Langmuir probe discharge data

Plasma models are crucial to gain physical insights into complex discharges as well as to optimizing plasma-driven processes. As an alternative to physical model, a qualitative model was constructed using adaptive fuzzy logic called adaptive network fuzzy inference system (ANFIS). Prediction performance of ANFIS was evaluated on two sets of experimental discharge data. One referred to as hemispherical inductively coupled plasma (HICP) was characterized with a 2(4) full factorial experiment, in which the factors that were varied include source power, pressure, chuck position, and Cl2 flow rate. The other called multipole ICP was characterized by performing a 3(3) full factorial experiment on the factors, including source power, pressure, and Ar flow rate. Trained ANFIS models were tested on eight and 16 experiments not pertaining to previous training data for HICP and MICP, respectively. Plasma attributes modeled include electron density. electron temperature, and plasma potential. The performance of ANFIS was optimized as a function of a type of membership function, number of membership function, and two learning factors. The number of membership functions was different depending on the type of plasma data and employing too large number of membership functions resulted in a drastic degradation in prediction performances. Optimized ANFIS models were compared to statistical regression models and demonstrated improved predictions in all comparisons.     

基于多智能算法混凝土强度预测系统设计

由于高强混凝土配方数据构成的复杂性,直接利用神经网络模型对其强度进行预测时存在精确度较差的问题。该文应用聚类分析原理对样本数据进行自身特性分析,然后对其自然分组,在此基础上建立多个BP神经网络拟合模型。强度预测时,根据实际数据自适应选择拟和模型,这样在大样本量混凝土强度预测中得到了较好的精度。     

Linear genetic programming for shear strength prediction of reinforced concrete beams without stirrups

A new design equation is proposed for the prediction of shear strength of reinforced concrete (RC) beams without stirrups using an innovative linear genetic programming methodology. The shear strength was formulated in terms of several effective parameters such as shear span to depth ratio, concrete cylinder strength at date of testing, amount of longitudinal reinforcement, lever arm, and maximum specified size of coarse aggregate. A comprehensive database containing 1938 experimental test results for the RC beams was gathered from the literature to develop the model. The performance and validity of the model were further tested using several criteria. An efficient strategy was considered to guarantee the generalization of the proposed design equation. For more verification, sensitivity and parametric analysis were conducted. The results indicate that the derived model is an effective tool for the estimation of the shear capacity of members without stirrups (R = 0.921). The prediction performance of the proposed model was found to be better than that of several existing buildings codes.     

Computer-aided strength prediction using the articulated total body model

The Articulated Total Body (ATB) Model, based on rigid-body dynamics with Euler equations of motion and Lagrange type constraints, has been used to predict the forces and motions experienced by air crew personnel in flight operations. But can be adapted to less abrupt stresses as experienced by typical industrial workers. To provide a more realistic representation of human dynamics, an active neuromusculature was added to the ATB Model via the newly developed advanced harness system. Furthermore, the ATB Model was used to simulate the whole body response to lateral forces utilizing trunk musculature. Although the musculature did not completely prevent the lateral deflection of the body, the response was significantly delayed compared to a control response, with the head and neck maintaining the upright posture for a longer period of time.     

Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction

Accurate prediction of high performance concrete (HPC) compressive strength is very important issue. In the last decade, a variety of modeling approaches have been developed and applied to predict HPC compressive strength from a wide range of variables, with varying success. The selection, application and comparison of decent modeling methods remain therefore a crucial task, subject to ongoing researches and debates. This study proposes three different ensemble approaches: (i) single ensembles of decision trees (DT) (ii) two-level ensemble approach which employs same ensemble learning method twice in building ensemble models (iii) hybrid ensemble approach which is an integration of attribute-base ensemble method (random sub-spaces RS) and instance-base ensemble methods (bagging Bag, stochastic gradient boosting GB). A decision tree is used as the base learner of ensembles and its results are benchmarked to proposed ensemble models. The obtained results show that the proposed ensemble models could noticeably advance the prediction accuracy of the single DT model and for determining average determination of correlation, the best models for HPC compressive strength forecasting are GB–RS DT, RS–GB DT and GB–GB DT among the eleven proposed predictive models, respectively. The obtained results show that the proposed ensemble models could noticeably advance the prediction accuracy of the single DT model and for determining determination of correlation (R²_max), the best models for HPC compressive strength forecasting are GB–RS DT (R²=0.9520), GB–GB DT (R²=0.9456) and Bag–Bag DT (R²=0.9368) among the eleven proposed predictive models, respectively.     

Proposition of new computer artificial intelligence models for shear strength prediction of reinforced concrete beams

Engineering with Computers - The study is investigated the capacity of new artificial intelligence (AI) methodologies for shear strength (Vs) computation of reinforced concrete (RC) beams. The...     

Fuzzy logic models for ranking process effects

When modeling and analyzing manufacturing processes, it may be helpful to know the relative importance of the various process parameters and their interactions. This ranking has traditionally been accomplished through regression modeling and analysis of variance (ANOVA). In this paper, we develop a fuzzy logic modeling technique to rank the importance of process effects. Several different cases are presented using functions that allow the determination of the actual importance of effects. The impact of noisy data on the results is considered for each case. It is shown that in many cases the fuzzy logic model (FLM) ranking methodology is capable of ranking process effects in the exact order or in an order reasonably close to the exact order. For complex processes where regression modeling and ANOVA techniques fail or require significant knowledge of the process to succeed, it is shown that the FLM-based ranking can be performed successfully with little or no knowledge of the process     

Inductive logic programming for gene regulation prediction

We present a systems biology application of ILP, where the goal is to predict the regulation of a gene under a certain condition from binding site information, the state of regulators, and additional information. In the experiments, the boosted Tilde model is on par with the original model by Middendorf et al. based on alternating decision trees (ADTrees), given the same information. Adding functional categorizations and protein-protein interactions, however, it is possible to improve the performance substantially. We believe that decoding the regulation mechanisms of genes is an exciting new application of learning in logic, requiring data integration from various sources and potentially contributing to a better understanding on a system level. Editors: Stephen Muggleton, Ramon Otero, Simon Colton.     

Fuzzy logic explains the golden proportion

It is known that aesthetically pleasing works of art, music, etc. contain a ratio known as the golden proportion. There have been many attempts to understand why this ratio is pleasing; however, there is no convincing and universally accepted explanation. In this article, we provide an explanation based on fuzzy logic. © 1997 John Wiley & Sons, Inc.     

Fuzzy logic alternative for analysis in the biomedical sciences.

Fuzzy logic brings new possibilities into control, modeling, data analysis, diagnostics, decision making, and other working fields in biomedical sciences. This paper presents how fuzzy logic can be used as an alternative or supplement to statistics in biomedical analysis. It shows an adaptive neuro-fuzzy inference computing in comparison with linear and curvilinear regression. The main goal of this presentation is to involve fuzzy logic in biomedical research. Thus, we carried out a mathematical treatment of the clinical sample, semen of infertile man, with the independent variable Concentration of spermatozoa and the dependent variable Number of spermatozoa by 230 observations.