REVISITING SYSTEMS AND APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS IN CONSTRUCTION ENGINEERING AND MANAGEMENT

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Alireza Shojaei ◽  
Amirsaman Mahdavian
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Construction Industry ◽  
Construction Engineering ◽  
Existing Problems ◽  
Modeling Methods ◽  
Recent Developments ◽  
Clustering And Classification ◽  
Artificial Neural ◽  
Complex Relationships

Artificial neural networks have been widely used for modeling and simulation of different problems in the construction industry, including, but not limited to, regression, clustering, and classification. They provide solutions for complex problems where other modeling methods often fail. For instance, they can capture nonlinear and complex relationships between the variables while many traditional modeling methods fail. However, they have their own limitations. They often can only be trained for a specific problem with a predetermined number of inputs and outputs. As a result, any change that requires an update in the architecture of the network cannot be automatically done and require human intervention. The recent developments in the field of artificial neural networks resulted in new concepts such as neural architecture search, reinforcement learning, and neuroevolution. These new areas can provide new methods for solving past and existing problems facing the construction industry in a more efficient, elegant, and versatile manner. One of the main contributions of the recent developments is networks that can optimize their own architecture and networks that are able to evolve and change their architecture. This paper aims to briefly review the application areas of the artificial neural networks in construction engineering and management and discuss how the recent developments in this field can be applied and provide better solutions.

scholarly journals Applying Artificial Neural Networks In Construction

2020 ◽  
Vol 143 ◽  
pp. 01029
Author(s):  
Anna Doroshenko
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Construction Industry ◽  
Structural Design ◽  
Soil Mechanics ◽  
Construction Materials ◽  
Construction Engineering ◽  
Design And Optimization ◽  
Artificial Neural

Currently, artificial neural networks (ANN) are used to solve the following complex problems: pattern recognition, speech recognition, complex forecasts and others. The main applications of ANN are decision making, pattern recognition, optimization, forecasting, data analysis. This paper presents an overview of applications of ANN in construction industry, including energy efficiency and energy consumption, structural analysis, construction materials, smart city and BIM technologies, structural design and optimization, application forecasting, construction engineering and soil mechanics.

Artificial Neural Networks and Their Applications in Business

2018 ◽  
pp. 6642-6657
Author(s):  
Trevor J. Bihl ◽  
William A. Young II ◽  
Gary R. Weckman
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Big Data ◽  
Artificial Neural Networks ◽  
Model Complexity ◽  
Ann Model ◽  
Processing Methods ◽  
Starting Point ◽  
Clustering And Classification ◽  
Artificial Neural

Despite the natural advantage humans have for recognizing and interpreting patterns, large and complex datasets, as in Big Data, preclude efficient human analysis. Artificial neural networks (ANNs) provide a family of pattern recognition approaches for prediction, clustering and classification applicable to KDD with ANN model complexity ranging from simple (for small problems) highly complex (for large issues). To provide a starting point for readers, this chapter first describes foundational concepts that relate to ANNs. A listing of commonly used ANN methods, heuristics, and criteria for initializing ANNs is then discussed. Common pre- and post- data processing methods for dimensionality reduction and data quality issues are then described. The authors then provide a tutorial example of ANN analysis. Finally, the authors list and describe applications of ANNs to specific business related endeavors for further reading.

Artificial Neural Networks and Their Applications in Business

2019 ◽  
pp. 1009-1027
Author(s):  
Trevor J. Bihl ◽  
William A. Young II ◽  
Gary R. Weckman
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Big Data ◽  
Artificial Neural Networks ◽  
Model Complexity ◽  
Ann Model ◽  
Processing Methods ◽  
Starting Point ◽  
Clustering And Classification ◽  
Artificial Neural

Despite the natural advantage humans have for recognizing and interpreting patterns, large and complex datasets, as in big data, preclude efficient human analysis. Artificial neural networks (ANNs) provide a family of pattern recognition approaches for prediction, clustering, and classification applicable to KDD with ANN model complexity ranging from simple (for small problems) to highly complex (for large issues). To provide a starting point for readers, this chapter first describes foundational concepts that relate to ANNs. A listing of commonly used ANN methods, heuristics, and criteria for initializing ANNs are then discussed. Common pre- and post-data processing methods for dimensionality reduction and data quality issues are then described. The authors then provide a tutorial example of ANN analysis. Finally, the authors list and describe applications of ANNs to specific business-related endeavors for further reading.

Comparison of Logistic Regression and Artificial Neural Network based Bankruptcy Prediction Models

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Easwaran Iyer ◽  
Vinod Kumar Murti
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Manufacturing Companies ◽  
Recent Developments ◽  
Artificial Neural

Logistic Regression is one of the popular techniques used for bankruptcy prediction and its popularity is attributed due to its robust nature in terms of data characteristics. Recent developments have explored Artificial Neural Networks for bankruptcy prediction. In this study, a paired sample of 174 cases of Indian listed manufacturing companies have been used for building bankruptcy prediction models based on Logistic Regression and Artificial Neural Networks. The time period of study was year 2000 through year 2009. The classification accuracies have been compared for built models and for hold-out sample of 44 paired cases. In analysis and hold-out samples, both the models have shown appreciable classification results, three years prior to bankruptcy. Thus, both the models can be used (by banks, SEBI etc.) for bankruptcy prediction in Indian Context, however, Artificial Neural Network has shown marginal supremacy over Logistic Regression.

Evolutionary Approaches for ANNs Design

2011 ◽  
pp. 575-580
Author(s):  
Antonia Azzini ◽  
Andrea G.B. Tettamanzi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Algorithms ◽  
Computational Models ◽  
Artificial Neurons ◽  
Important Research Topic ◽  
Recent Developments ◽  
Evolving System ◽  
Artificial Neural ◽  
Ann Application

Artificial neural networks (ANNs) are computational models, loosely inspired by biological neural networks, consisting of interconnected groups of artificial neurons which process information using a connectionist approach. ANNs are widely applied to problems like pattern recognition, classification, and time series analysis. The success of an ANN application usually requires a high number of experiments. Moreover, several parameters of an ANN can affect the accuracy of solutions. A particular type of evolving system, namely neuro-genetic systems, have become a very important research topic in ANN design. They make up the so-called Evolutionary Artificial Neural Networks (EANNs), i.e., biologicallyinspired computational models that use evolutionary algorithms (EAs) in conjunction with ANNs. Evolutionary algorithms and state-of-the-art design of EANN were introduced first in the milestone survey by Xin Yao (1999), and, more recently, by Abraham (2004), by Cantu-Paz and Kamath (2005), and then by Castellani (2006). The aim of this article is to present the main evolutionary techniques used to optimize the ANN design, providing a description of the topics related to neural network design and corresponding issues, and then, some of the most recent developments of EANNs found in the literature. Finally a brief summary is given, with a few concluding remarks.

scholarly journals A Methodological Proposal Based on Artificial Neural Networks for Evapotranspiration Assessment

2017 ◽  
Vol 9 (5) ◽  
pp. 142
Author(s):  
Alberto B. Mirambell ◽  
Clayton F. da Silva ◽  
Flavio De Souza Barbosa ◽  
Celso Bandeira de Melo Ribeiro
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Crop Evapotranspiration ◽  
Reference Crop Evapotranspiration ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
Complex Relationships ◽  
Hidden Layer ◽  
Reference Crop ◽  
Hidden Nodes

Evapotranspiration is the combined process in which water is transferred from the soil by evaporation and through the plants by transpiration to the atmosphere. Therefore, it is a central parameter in Agriculture since it expresses the amount of water to be returned by irrigation. Aiming to standardize Evapotranspiration estimate, the term “reference crop evapotranspiration (ETo)” was coined as the rate of Evapotranspiration from a hypothetical grass surface of uniform height, actively growing, completely shading the ground and well watered. ETo can be measured with lysimeters or estimated by mathematical approaches. Although, Penman-Monteith FAO 56 (PM) is the recommended method to estimate ETo by PM, it is necessary to register maximum and minimum temperatures (ºC), solar radiation (hours), relative humidity (%) and wind speed (m/seg.). Some of these parameters are missing in the historical meteorological registers. Here, Artificial Neural Networks (ANNs) can aid traditional methodologies. ANNs learn, recognise patterns and generalise complex relationships among large datasets to produce meaningful results even when input data is wrong or incomplete. The target of this study is to assess ANNs capability to estimatie ETo values. We have built and tested several architectures guided by Levenberg-Marquardt algorithm with 5 above mentioned parameters as inputs, from 1 to 50 hidden nodes and 1 parameter as output. Architectures with 10, 15 and 20 nodes in the hidden layer brought outsanding r2 values: 0.935, 0.937, 0.937 along with the highest intercept and the lowest slope values, which demonstrate that ANNs approach was an afficient method to estimate ETo.

scholarly journals Text Recognition with Artificial Neural Networks and Open CV

2019 ◽  
Vol 8 (2) ◽  
pp. 5525-5528
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Feature Learning ◽  
Text Recognition ◽  
Unsupervised Feature Learning ◽  
Recent Developments ◽  
Multilayer Neural Networks ◽  
Artificial Neural ◽  
Representational Power ◽  
Standard Framework

Recognizing text in images has received attention recently. Traditional systems during this space have relied on elaborating models incorporating rigorously hand-designed options or giant amounts of previous information. This paper proposed by taking a different route and combines the representational power of large, multilayer neural networks together with recent developments in unsupervised feature learning, which allows us to use a standard framework to coach highly accurate character recognizer and text detector modules. The recognition pipeline of scanning, segmenting, and recognition is examined and delineated completely

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