A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model

Engineering with Computers - Recycled aggregate concrete is used as an alternative material in construction engineering, aiming to environmental protection and sustainable development. However, the...     

A priori evaluation of simulation models preparation processes using artificial intelligence techniques

Controlling the well-known triptych costs, quality and time during the different phases of the Product Development Process (PDP) is an everlasting challenge for the industry. Among the numerous issues that are to be addressed, the development of new methods and tools to adapt to the various needs the models used all along the PDP is certainly one of the most challenging and promising improvement area. This is particularly true for the adaptation of Computer-Aided Design (CAD) models to Computer-Aided Engineering (CAE) applications, and notably during the CAD models simplification steps. Today, even if methods and tools exist, such a preparation phase still requires a deep knowledge and a huge amount of time when considering Digital Mock-Up (DMU) composed of several hundreds of thousands of parts. Thus, being able to estimate a priori the impact of DMU adaptation scenarios on the simulation results would help identifying the best scenario right from the beginning. This paper addresses such a difficult problem and uses artificial intelligence (AI) techniques to learn and accurately predict behaviours from carefully selected examples. The main idea is to identify rules from these examples used as inputs of learning algorithms. Once those rules obtained, they can be used on a new case to a priori estimate the impact of a preparation process without having to perform it. To reach this objective, a method to build a representative database of examples has been developed, the right input (explanatory) and output (preparation process quality criteria) variables have been identified, then the learning model and its associated control parameters have been tuned. One challenge was to identify explanatory variables from geometrical key characteristics and data characterizing the preparation processes. A second challenge was to build a effective learning model despite a limited number of examples. The rules linking the output variables to the input ones are obtained using AI techniques such as well-known neural networks and decision trees. The proposed approach is illustrated and validated on industrial examples in the context of computational fluid dynamics simulations.     

Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model

The design and sustainability of reinforced concrete deep beam are still the main issues in the sector of structural engineering despite the existence of modern advancements in this area. Proper understanding of shear stress characteristics can assist in providing safer design and prevent failure in deep beams which consequently lead to saving lives and properties. In this investigation, a new intelligent model depending on the hybridization of support vector regression with bio-inspired optimization approach called genetic algorithm (SVR-GA) is employed to predict the shear strength of reinforced concrete (RC) deep beams based on dimensional, mechanical and material parameters properties. The adopted SVR-GA modelling approach is validated against three different well established artificial intelligent (AI) models, including classical SVR, artificial neural network (ANN) and gradient boosted decision trees (GBDTs). The comparison assessments provide a clear impression of the superior capability of the proposed SVR-GA model in the prediction of shear strength capability of simply supported deep beams. The simulated results gained by SVR-GA model are very close to the experimental ones. In quantitative results, the coefficient of determination (R²) during the testing phase (R² = 0.95), whereas the other comparable models generated relatively lower values of R² ranging from 0.884 to 0.941. All in all, the proposed SVR-GA model showed an applicable and robust computer aid technology for modelling RC deep beam shear strength that contributes to the base knowledge of material and structural engineering perspective.

     

Kinematic analysis of a novel 3-DOF actuation redundant parallel manipulator using artificial intelligence approach

Kinematic analysis is one of the key issues in the research domain of parallel kinematic manipulators. It includes inverse kinematics and forward kinematics. Contrary to a serial manipulator, the inverse kinematics of a parallel manipulator is usually simple and straightforward. However, forward kinematic mapping of a parallel manipulator involves highly coupled nonlinear equations. Therefore, it is more difficult to solve the forward kinematics problem of parallel robots. In this paper, a novel three degrees-of-freedom (DOFs) actuation redundant parallel manipulator is introduced. Different intelligent approaches, which include the Multilayer Perceptron (MLP) neural network, Radial Basis Functions (RBF) neural network, and Support Vector Machine (SVM), are applied to investigate the forward kinematic problem of the robot. Simulation is conducted and the accuracy of the models set up by the different methods is compared in detail. The advantages and the disadvantages of each method are analyzed. It is concluded that ν-SVM with a linear kernel function has the best performance to estimate the forward kinematic mapping of a parallel manipulator.     

A comprehensive study on the effects of using data mining techniques to predict tie strength

The use of social networks has grown noticeably in recent years and this fact has led to the production of numerous volumes of data. Data that are widely used by users on the social media sites are very large, noisy, unstructured and dynamic. Providing a flexible framework and method to apply in all of these networks can be the perfect solution. The uncertainties arising from the complexity of decisions in recognition of the Tie Strength among people have led researchers to seek effective variables of intimacy among people. Since there are several effective variables which their effectiveness rate are not precisely determined and their relations are nonlinear and complex, using data mining techniques can be considered as one of the practical solutions for this problem. Some types of unsupervised mining methods have been conducted in the field of detecting the type of tie. Data mining could be considered as one of the applicable tools for researchers in exploring the relationships among users.In this paper, the problem of tie strength prediction is modeled as a data mining problem on which different supervised and unsupervised mining methods are applicable. We propose a comprehensive study on the effects of using different classification techniques such as decision trees, Naive Bayes and so on; in addition to some ensemble classification methods such as Bagging and Boosting methods for predicting tie strength of users of a social network. LinkedIn social network is used as a real case study and our experimental results are proposed on its extracted data. Several models, based on basic techniques and ensemble methods are created and their efficiencies are compared based on F-Measure, accuracy, and average executing time. Our experimental results show that, our profile-behavioral based model has much better accuracy in comparison with profile-data based models techniques.     

An artificial intelligence approach to language instruction

This paper describes an implemented, prototype system for a sophisticated, intelligent tutor for instruction in a foreign language. The system is an application of artificial intelligence research in natural language, but it implements several ideas that depart from standard approaches to natural language understanding.

For instance, the semantic analyzer diagnoses several kinds of comprehension problems and semantic errors that a student might make. Some fine distinctions in meaning are represented to detect misuse of words. Not only is a model of good syntax included in the tutor, but also a model of incorrect forms, rich enough to pinpoint specific syntactic mistakes. Finding the intended interpretation is complicated by the likelihood of student errors. Therefore, perfect syntactic form is not necessary for semantic analysis of the student's input.

The problems discussed and solutions presented are closely related to the more general problem of how to respond to a natural language input that surpasses the computer's model of language or of context.     

Hybrid artificial intelligence approach to urban planning

Knowledge-based modeling and implementation of the various urban planning processes represent an intensive research area. This paper presents a hybrid artificial intelligence system using a knowledge-based approach, neural networks and fuzzy logic that automates the decision-making process in urban planning. The system is used for developing urban development alternatives based on real-world data. Results show that, by integrating knowledge-based systems, artificial neural networks and fuzzy systems, the system achieves improvements in the implementation of each respective system as well as an increase in the breadth of functionality within the application. With this approach, the best of three technologies can be compiled together to solve complex urban problems. We discuss the structure of the combined technologies, as well as providing examples of its application in the field of urban development.     

A novel artificial intelligence technique for analyzing slope stability using PSO-CA model

This study aims to develop a new artificial intelligence model for analyzing and evaluating slope stability in open-pit mines. Indeed, a novel hybrid intelligent technique based on an optimization of the cubist algorithm by an evolutionary method (i.e., PSO), namely PSO-CA technique, was developed for predicting the factor of safety (FS) in slope stability; 450 simulations from the Geostudio software for the FS of a quarry mine (Vietnam) were used as the datasets for this aim. Five factors include bench height, slope angle, angle of internal friction, cohesion, and unit weight were used as the input variables for estimating FS in this work. To clarify the performance of the proposed PSO-CA technique in slope stability analysis, SVM, CART, and kNN models were also developed and assessed. Three performance indices, such as mean absolute error (MAE), root-mean-squared error (RMSE), and determination coefficient (R²), were computed to evaluate the accuracy of the predictive models. The results clarified that the proposed PSO-CA technique was the most dominant accuracy with an MAE of 0.009, RMSE of 0.025, and R² of 0.981, in estimating the stability of slope. The remaining models (i.e., SVM, CART, kNN) obtained poorer performance with MAE from 0.014 to 0.038, RMSE 0.030–0.056, and R² 0.917–0.974.

     

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...     

Evaluating the performance of a EuroDivisia index using artificial intelligence techniques

This paper compares two methods to predict inflation rates in Europe. One method uses a standard back propagation neural network and the other uses an evolutionary approach, where the network weights and the network architecture are evolved. Results indicate that back propagation produces superior results. However, the evolving network still produces reasonable results with the advantage that the experimental set-up is minimal. Also of interest is the fact that the Divisia measure of money is superior as a predictive tool over simple sum.     

Generating educational mobile applications using UIDPs identified by artificial intelligence techniques

Commercial mobile devices vary in brand, size, and functionalities, but they all allow people to interact with educational applications. In software engineering, application development techniques, approaches, methodologies, and processes (e.g., BBD, FDD, RAD, DDD) are often time consuming, costly, or aim at expert developers – which implies that users outside the software development field (e.g., teachers) need great practice to become experienced application developers. This work proposes an artificial-intelligence-based process for generating educational mobile apps from freehand-generated images. The images’ design is based on User Interface Design Pattern (UIDP) representations. As a proof of concept, we introduce EduMatic, an application development tool. To test our process, we assessed Wiki-Comp, an application built with EduMatic, along with three other external educational applications: Khan Academy, Wiki encyclopedia, and Kiwix. According to the evaluation results, Wiki-Comp outstands in functionality, usability, and performance aspects.     

Prediction of quality characteristics of laser drilled holes using artificial intelligence techniques

Micro-drilling using lasers finds widespread industrial applications in aerospace, automobile, and bio-medical sectors for obtaining holes of precise geometric quality with crack-free surfaces. In order to achieve holes of desired quality on hard-to-machine materials in an economical manner, computational intelligence approaches are being used for accurate prediction of performance measures in drilling process. In the present study, pulsed millisecond Nd:YAG laser is used for micro drilling of titanium alloy and stainless steel under identical machining conditions by varying the process parameters such as current, pulse width, pulse frequency, and gas pressure at different levels. Artificial intelligence techniques such as adaptive neuro-fuzzy inference system (ANFIS) and multi gene genetic programming (MGGP) are used to predict the performance measures, e.g. circularity at entry and exit, heat affected zone, spatter area and taper. Seventy percent of the experimental data constitutes the training set whereas remaining thirty percent data is used as testing set. The results indicate that root mean square error (RMSE) for testing data set lies in the range of 8.17–24.17% and 4.04–18.34% for ANFIS model MGGP model, respectively, when drilling is carried out on titanium alloy work piece. Similarly, RMSE for testing data set lies in the range of 13.08–20.45% and 6.35–10.74% for ANFIS and MGGP model, respectively, for stainless steel work piece. Comparative analysis of both ANFIS and MGGP models suggests that MGGP predicts the performance measures in a superior manner in laser drilling operation and can be potentially applied for accurate prediction of machining output.

     

Segmentation of a seismic section using image processing and artificial intelligence techniques

Methods for segmenting stacked seismic data into zones of common signal character based on texture analysis are described. Their performance is demonstrated on a line of seismic data from the Gulf of Mexico that had been manually segmented. Two segmentation methods are described. The first is a template matching scheme that matches previously selected data templates with a block of pixels. The second uses statistics determined by examining the run-length of seismic reflection events. The run-length method is extended, through a decision process called the RESOLVER, to incorporate heuristic rules to influence the segmentation. A comparison is made between the automatic segmentations of the section and a manual interpretation.     

Using object concepts to match artificial intelligence techniques to problem types

Using object-concepts as a matching framework, we provide guidelines for identifying what types of problems are best served by which knowledge-representation technique. We find that production rules are best for hierarchical classification problems, because they support classification/instantiation of data. Frames are best for data retrieval and inference problems, because, using data abstraction, frames can operate on data within a frame. Finally, semantic networks are best for consequence finding problems, because of independence of the primitives in the hierarchy. Providing guidelines for this matching is important, because the success of different information systems designs have been shown to depend explicitly on problem characteristics.     

Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals

Secondary phases such as Laves and carbides are formed during the final solidification stages of nickel based superalloy coatings deposited during the gas tungsten arc welding cold wire process. However, when aged at high temperatures, other phases can precipitate in the microstructure, like the γ″ and δ phases. This work presents a new application and evaluation of artificial intelligent techniques to classify (the background echo and backscattered) ultrasound signals in order to characterize the microstructure of a Ni-based alloy thermally aged at 650 and 950 °C for 10, 100 and 200 h. The background echo and backscattered ultrasound signals were acquired using transducers with frequencies of 4 and 5 MHz. Thus with the use of features extraction techniques, i.e., detrended fluctuation analysis and the Hurst method, the accuracy and speed in the classification of the secondary phases from ultrasound signals could be studied. The classifiers under study were the recent optimum-path forest (OPF) and the more traditional support vector machines and Bayesian. The experimental results revealed that the OPF classifier was the fastest and most reliable. In addition, the OPF classifier revealed to be a valid and adequate tool for microstructure characterization through ultrasound signals classification due to its speed, sensitivity, accuracy and reliability.     

An artificial intelligence planning approach to manufacturing feature recognition

Within manufacturing, features have been widely accepted as useful concepts, and in particular they are used as an interface between CAD and CAPP systems. Previous research on feature recognition focus on the issues of intersecting features and multiple interpretations, but do not address the problem of custom features representation. Representation of features is an important aspect for making feature recognition more applicable in practice. In this paper a hybrid procedural and knowledge-based approach based on artificial intelligence planning is presented, which addresses both classic feature interpretation and also feature representation problems. STEP designs are presented as case studies in order to demonstrate the effectiveness of the model.     

An artificial intelligence approach to speech recognition and understanding

This paper presents the I.R.S.T. Automatic Speech Recognition and Understanding (A.S.R.U.) Research Program for continuously spoken Italian without previous knowledge of the identity of the speaker. The acoustic analysis is performed in time domain and works in real-time. Acoustic ambiguities are overcome by using various levels of contextual information (orthophonic, syntactic, semantic) to formulate hypotheses to be verified by means of an hypothesize and test paradigm. The architecture is an analysis by synthesis loop.     

Cognitive robotics: a new approach to artificial intelligence

This paper describes a research program about how to achieve artificial intelligence by building robots. It is part of the behavior-oriented AI approach, but differs in some of its hypotheses and methodological approach. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998