• Smart Structures and Systems
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• v.26 no.2
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• pp.241-251
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• 2020

Due to the undeniable importance of approximating the concrete compressive strength (CSC) in civil engineering, this paper focuses on presenting four novel optimizations of multi-layer perceptron (MLP) neural network, namely artificial bee colony (ABC-MLP), grasshopper optimization algorithm (GOA-MLP), shuffled frog leaping algorithm (SFLA-MLP), and salp swarm algorithm (SSA-MLP) for predicting this crucial parameter. The used dataset consists of 103 rows of information concerning seven influential parameters (cement, slag, water, fly ash, superplasticizer, fine aggregate, and coarse aggregate). In this work, the best-fitted complexity of each ensemble is determined by a population-based sensitivity analysis. The GOA distinguished its self by the least complexity (population size = 50) and emerged as the second time-effective optimizer. Referring to the prediction results, all tested algorithms are able to construct reliable networks. However, the SSA (Correlation = 0.9652 and Error = 1.3939) and GOA (Correlation = 0.9629 and Error = 1.3922) performed more accurately than ABC (Correlation = 0.7060 and Error = 4.0161) and SFLA (Correlation = 0.8890 and Error = 2.5480). Therefore, the SSA-MLP and GOA-MLP can be promising alternatives to laboratorial and traditional CSC evaluative methods.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.2
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• pp.71-77
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• 2015

Upcoming ground-breaking applications for always-on tiny interconnected devices steadily demand two-fold features of processor cores: aggressively low power consumption and enhanced performance. We propose implementation of a novel superscalar low-power processor core with a low supply voltage. The core implements intra-core low-power microarchitecture with minimal performance degradation in instruction fetch, branch prediction, scheduling, and execution units. The inter-core lockstep not only detects malfunctions during low-voltage operation but also carries out software-based recovery. The chip incorporates a pair of cores, high-speed memory, and peripheral interfaces to be implemented with a 65nm node. The processor core consumes only 24mW at 350MHz and 0.68V, resulting in power efficiency of $80{\mu}W/MHz$. The operating frequency of the core reaches 850MHz at 1.2V.

• Wind and Structures
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• v.2 no.1
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• pp.1-23
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• 1999

Wind effects are critical considerations in the design of topside structures, overall structural systems, or both, depending on the water depth and type of offshore platform. The reliable design of these facilities for oil fields in regions of hostile environment can only be assured through better understanding of the environmental load effects and enhanced response prediction capabilities. This paper summarizes the analysis and performance of offshore platforms under extreme wind loads, including the quantification of wind load effects with focus on wind field characteristics, steady and unsteady loads, gust loading factors, application of wind tunnel tests, and the provisions of the American Petroleum Institute Recommended Practice 2A - Working Stress Design (API RP 2A-WSD) for the construction of offshore structures under the action of wind. A survey of the performance of platforms and satellite structures is provided, and failure mechanisms concerning different damage scenarios during Hurricane Andrew are examined. Guidelines and provisions for improving analysis and design of structures are addressed.

• Structural Engineering and Mechanics
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• v.72 no.4
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• pp.479-489
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• 2019

The corrosion of reinforcement leads to a gradual decay of structural strength and durability. Several models for crack occurrence prediction and crack width propagation are investigated in this paper. Analytical and experimental models were used to predict the bond strength in the period of corrosion propagation. The manner of flexural strength loss is calculated by application of these models for different scenarios. As a new approach, the variation of the concrete beam neutral axis height has been evaluated, which shows a reduction in the neutral axis height for the scenarios without loss of bond. Alternatively, an increase of the neutral axis height was observed for the scenarios including bond and concrete section loss. The statistical properties of the parameters influencing the strength have been deliberated associated with obtaining the time-dependent bending strength during corrosion propagation, using Monte Carlo (MC) random sampling method. Results showed that the ultimate strain in concrete decreases significantly as a consequence of the bond strength reduction during the corrosion process, when the section reaches to its final limit. Therefore, such sections are likely to show brittle behavior.

• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.259-272
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• 2022

The branch of electronics that uses an organic solar cell or conductive organic polymers in order to yield electricity from sunlight is called photovoltaic. Regarding this crucial issue, an artificial intelligence-based predictor is presented to investigate the vibrational behavior of the organic solar cell. In addition, the generalized differential quadrature method (GDQM) is utilized to extract the results. The validation examination is done to confirm the credibility of the results. Then, the deep neural network with fully connected layers (DNN-FCL) is trained by means of Adam optimization on the dataset whose members are the vibration response of the design-points. By determining the optimum values for the biases along with weights of DNN-FCL, one can predict the vibrational characteristics of any organic solar cell by knowing the properties defined as the inputs of the mentioned DNN. To assess the ability of the proposed artificial intelligence-based model in prediction of the vibrational response of the organic solar cell, the authors monitored the mean squared error in different steps of the training the DNN-FCL and they observed that the convergency of the results is excellent.

• Advances in nano research
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• v.11 no.6
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• pp.595-606
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• 2021

This paper conducted mainly for forecasting the behavior of the shear connectors in steel-concrete composite beams based on the different factors. The main goal was to analyze the influence of variable parameters on the shear strength of C-shaped and L-shaped angle shear connectors. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data in order to select the most influential factors for the mentioned shear strength forecasting. Five inputs are considered: height, length, thickness of shear connectors together with concrete strength and respective slip of the shear connectors after testing. The ANFIS process for variable selection was also implemented in order to detect the predominant factors affecting the forecasting of the shear strength of C-shaped and L-shaped angle shear connectors. The results show that the forecasting methodology developed in this research is useful for enhancing the multiple performances characterizing in the shear strength prediction of C and L shaped angle shear connectors analyzing.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of Microbiology and Biotechnology
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• v.19 no.7
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• pp.718-726
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• 2009

The objective of this study was to develop and validate secondary models that can predict growth parameters of L. monocytogenes Scott A as a function of concentrations (0-3%) of a commercial potassium lactate (PL) and sodium diacetate (SDA) mixture, pH (5.5-7.0), and temperature (4-37DC). A total of 120 growth curves were fitted to the Baranyi primary model that directly estimates lag time (LT) and specific growth rate (SGR). The effects of the variables on L. monocytogenes Scott A growth kinetics were modeled by response surface analysis using quadratic and cubic polynomial models of the natural logarithm transformation of both LT and SGR. Model performance was evaluated with dependent data and independent data using the prediction bias ($B_f$) and accuracy factors ($A_f$) as well as the acceptable prediction zone method [percentage of relative errors (%RE)]. Comparison of predicted versus observed values of SGR indicated that the cubic model fits better than the quadratic model, particularly at 4 and $10^{\circ}C$. The $B_f$and $A_f$for independent SGR were 1.00 and 1.08 for the cubic model and 1.08 and 1.16 for the quadratic model, respectively. For cubic and quadratic models, the %REs for the independent SGR data were 92.6 and 85.7, respectively. Both quadratic and cubic polynomial models for SGR and LT provided acceptable predictions of L. monocytogenes Scott A growth in the matrix of conditions described in the present study. Model performance can be more accurately evaluated with $B_f$and $A_f$and % RE together.

• Therapeutic Science for Rehabilitation
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• v.11 no.4
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• pp.23-39
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• 2022

Objective : To summarize clinical and demographic variables and machine learning uses for predicting functional outcomes of patients with stroke. Methods : We searched PubMed, CINAHL and Web of Science to identify published articles from 2010 to 2021. The search terms were "machine learning OR data mining AND stroke AND function OR prediction OR/AND rehabilitation". Articles exclusively using brain imaging techniques, deep learning method and articles without available full text were excluded in this study. Results : Nine articles were selected for this study. Support vector machines (19.05%) and random forests (19.05%) were two most frequently used machine learning models. Five articles (55.56%) demonstrated that the impact of patient initial and/or discharge assessment scores such as modified ranking scale (mRS) or functional independence measure (FIM) on stroke patients' functional outcomes was higher than their clinical characteristics. Conclusions : This study showed that patient initial and/or discharge assessment scores such as mRS or FIM could influence their functional outcomes more than their clinical characteristics. Evaluating and reviewing initial and or discharge functional outcomes of patients with stroke might be required to develop the optimal therapeutic interventions to enhance functional outcomes of patients with stroke.

• Computers and Concrete
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• v.26 no.4
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• pp.309-316
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• 2020

The application of multi-variable adaptive regression spline (MARS) in predicting he long-term compressive strength of a concrete with various admixtures has been investigated in this study. The compressive strength of concrete specimens, which were made based on 24 different mix designs using various mineral and chemical admixtures in different curing ages have been obtained. First, The values of fly ash (FA), micro-silica (MS), water-reducing admixture (WRA), coarse and fine aggregates, cement, water, age of samples and compressive strength were defined as inputs to the model, and MARS analysis was used to model the compressive strength of concrete and to evaluate the most important parameters affecting the estimation of compressive strength of the concrete. Next, the proposed equation by the MARS method using particle swarm optimization (PSO) algorithm has been optimized to have more efficient equation from the economical point of view. The proposed model in this study predicted the compressive strength of the concrete with various admixtures with a correlation coefficient of R=0.958 rather than the measured compressive strengths within the laboratory. The final model reduced the production cost and provided compressive strength by reducing the WRA and increasing the FA and curing days, simultaneously. It was also found that due to the use of the liquid membrane-forming compounds (LMFC) for its lower cost than water spraying method (SWM) and also for the longer operating time of the LMFC having positive mechanical effects on the final concrete, the final product had lower cost and better mechanical properties.