scholarly journals Macroeconomic Indicators as Potential Predictors of Construction Material Prices

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
N.M. Jesna ◽  
Diya K Dilip
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cost Estimation ◽  
Economic Status ◽  
Construction Material ◽  
Construction Materials ◽  
The Future ◽  
Macroeconomic Indicators ◽  
Artificial Neural ◽  
Material Price

The rate of construction materials is subjected to constant changes. The unexpected price changes affect the carrying-out rates of projects and even challenges the competence to finish the projects. The rapid and vast changes that occur all over the world in construction materials prices impacts the individual construction market value of each country. To avoid this problem, the contractor should have a tool or method that is capable to predict the future material prices. It is essential to predict the material prices variations during the implementation of the project as well as for preparing the tenders. Prediction of material price is an important function for effectively handling projects in terms of more exactly estimating, pursuing and monitoring projects. There are many tools that can help the construction contractors by its ability to accurately predict the future material price. Some of the methods normally used for prediction of materials prices are Artificial Neural Network, Fuzzy Logic, Statistical Method (includes regression analysis, MONTE CARLO method, ANOVA), and Trend Analysis. The type of predictors to these tools can be any factors that tend to have an impact on the prices of material.  Macroeconomic indicators are one such factor that influences the prices of material as it reflects a country’s economic status. This is a pilot study conducted in India to determine the possible macroeconomic indicators that influence the building material prices namely Portland cement and steel. Keywords-Cost estimation, Artificial Neural Network, Macroeconomic indicator

Concrete strength prediction using artificial neural network and genetic programming

2018 ◽  
Vol 9 (3) ◽  
pp. 75
Author(s):  
Preeti Kulkarni ◽  
Shreenivas N. Londhe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Genetic Programming ◽  
Construction Material ◽  
Concrete Strength ◽  
Maximum Size ◽  
Design Parameters ◽  
Ann Model ◽  
Influencing Parameters ◽  
Artificial Neural

Concrete is a highly complex composite construction material and modeling using computing tools to predict concrete strength is a difficult task. In this work an effort is made to predict compressive strength of concrete after 28 days of curing, using Artificial Neural Network (ANN) and Genetic programming (GP). The data for analysis mainly consists of mix design parameters of concrete, coefficient of soft sand and maximum size of aggregates as input parameters. ANN yields trained weights and biases as the final model which sometime may impediment in its application at operational level. GP on other hand yields an equation as its output making its plausible tool for operational use. Comparison of the prediction results displays the result the model accuracy of both ANN and GP as satisfactory, giving GP a working advantage owing to its output in an equation form. A knowledge extraction technique used with the weights and biases of ANN model to understand the most influencing parameters to predict the 28 day strength of concrete, promises to prove ANN as grey box rather than a black box. GP models, in form of explicit equations, show the influencing parameters with reference to the presence of the relevant parameters in the equations.

scholarly journals An Artificial Neural Network Approach to Predict the Future Land Use Land Cover of Great Malang Region, Indonesia

2021 ◽  
Author(s):  
Fatwa Ramdani ◽  
Budi Setiawan ◽  
Alfi Rusydi ◽  
Muhammad Furqon
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Land Cover ◽  
Negative Impact ◽  
Population Increase ◽  
Land Use Land Cover ◽  
Neural Network Approach ◽  
The Future ◽  
Artificial Neural

Great Malang region is developing rapidly with the population increase and inhabitant`s activity, like migration and urbanization. Other activities like agricultural expansion as well as an uncontrolled residential development need to be monitored to avoid any negative impact in the future. The availability of free and open-source software, spatial high-resolution satellite imagery datasets, and powerful algorithms open the possibilities to map, monitor, and predict the future trend of land use land cover (LULC) changes. However, the accuracy and precision of this model is still in doubt, especially in the Great Malang region. Research is needed to provide a foundational basis and documentation on how the changes occur, where did the changes occur, and the accuracy of the predicted model. This study tries to answer those questions using the high spatial resolution of Sentinel-2 imageries. Combination of the fuzzy algorithm, artificial neural network, and cellular automata was utilized to process the datasets. We analysed four different scenarios of simulation and the result then compared. The different number of hidden layers and iteration was used and evaluated to understand the effect of different parameters in the prediction result. The best scenario was then used to predict future land use changes. This study has successfully produced the future LULC model of Great Malang region with high accuracy level (87%). The study also found that the land use transformation from agriculture to urban built-up area is relatively low, where changes of the built-up area over three periods of analysis are below than 5%. This is due to the physical condition of Great Malang region where mountainous areas are dominated.

scholarly journals Private Label and Macroeconomic Indexes: An Artificial Neural Networks Application

2020 ◽  
Vol 10 (17) ◽  
pp. 6043 ◽  
Author(s):  
Eloy Gil-Cordero ◽  
Juan-Pedro Cabrera-Sánchez
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Private Label ◽  
Macroeconomic Variables ◽  
Macroeconomic Indicators ◽  
Per Capita ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Macroeconomic Indicator

Retail companies operate with a private label assortment of 40–45% of their total assortment, which has led to a significant growth of private labels in recent years in their countries of origin; however, when retail companies decide to internationalize, it is important to know which macroeconomic indicators are more relevant when entering a new country or continent. For that reason, in this study we have as a main objective to establish which are the most transcendental macroeconomic variables for the volume and value of the private label. For this purpose, we have analyzed a total of 1400 samples, creating an artificial neural network (ANN). The results show that the most important macroeconomic indicator that must be taken into consideration above other macroeconomic indicators for retail companies to be successful within a country is the per capita debt. In addition, we have considered in this research that unemployment is not the most important primary indicator for the volume of the private label.

Neural Network Based Prediction Model for Job Applicants

2019 ◽  
Vol 16 (9) ◽  
pp. 3867-3873
Author(s):  
Sourav Thakial ◽  
Bhavna Arora
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Predictive Analytics ◽  
Fuzzy Neural Networks ◽  
Job Applicants ◽  
Support Vector ◽  
The Future ◽  
Artificial Neural

Predictive analytics, a division of the advanced analytics that uses various techniques like machine learning, data mining and so on, to predict the future events. Predictive analytics is summarized with the data collection, modelling, statistics and deployment. It can be used to predict the future possibilities in different areas like business, healthcare, telecom, finance. An effective technique for prediction is Artificial Neural Network. The model accuracy for prediction can be enhanced using neural networks. The model can also be used easily for prediction of output parameters because of its ability to solve the complex computation which are difficult to be solved by other techniques. In this paper, a brief review of Artificial Neural Network used for prediction analysis is presented with various techniques like Multi-Layer Perceptron, T-S Fuzzy Neural Networks, Support Vector Machine, Radial Basis Function Network, Levenberg-Marquardt Algorithm and Back Propagation and their applications are also presented. This paper also presents the neural network-based prediction model for job applicants which is used to predict the jobs of various applicants based on certain parameter ratings.

Sign in / Sign up

Export Citation Format

Share Document