scholarly journals Estimation of life-cycle costs of buildings: regression vs artificial neural network

2016 ◽  
Vol 6 (1) ◽  
pp. 30-43 ◽  
Author(s):  
Ayedh Alqahtani ◽  
Andrew Whyte
Keyword(s):  
Life Cycle ◽  
Cost Estimation ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Data Set ◽  
Building Projects ◽  
Content Type ◽  
Testing Stage ◽  
Running Cost ◽  
Artificial Neural

Purpose – The purpose of this paper is to compare the performance of regression and artificial-neural-networks (ANNs) methods to estimate the running cost of building projects towards improved accuracy. Design/methodology/approach – A data set of 20 building projects is used to test the performance of these two (ANNs/regression) models in estimating running cost. The concept of cost-significant-items is identified as important in assisting estimation. In addition, a stepwise technique is used to eliminate insignificant factors in regression modelling. A connection weight method is applied to determine the importance of cost factors in the performance of ANNs. Findings – The results illustrate that the value of the coefficient of determination=99.75 per cent for ANNs model(s), with a value of 98.1 per cent utilising multiple regression (MR) model(s); second, the mean percentage error (MPE) for ANNs at a testing stage is 0.179, which is less than that of the MPE gained through MR modelling of 1.28; and third, the average accuracy is 99 per cent for ANNs model(s) and 97 per cent for MR model(s). On the basis of these results, it is concluded that an ANNs model is superior to a MR model when predicting running cost of building projects. Research limitations/implications – A means for continuous improvement for the performance of the models accuracy has been established; this may be further enhanced by future extended sample. Originality/value – This work extends the knowledge base of life-cycle estimation where ANNs method has been found to reduce preparation time consumed and increasing accuracy improvement of the cost estimation.

scholarly journals Evaluation of non-cost factors affecting the life cycle cost: an exploratory study

2016 ◽  
Vol 14 (4) ◽  
pp. 818-834 ◽  
Author(s):  
Ayedh Alqahtani ◽  
Andrew Whyte
Keyword(s):  
Life Cycle ◽  
Survey Data ◽  
Cost Estimation ◽  
Life Cycle Cost ◽  
Accurate Estimation ◽  
Capital Operation ◽  
Building Projects ◽  
Content Type ◽  
Factors Affecting ◽  
Cost Factors

Purpose This paper aims to identify the main non-cost factors affecting accurate estimation of life cycle cost (LCC) in building projects. Design/methodology/approach Ten factors affecting LCC in building project cost estimates are identified through literature and interviews. A questionnaire survey is conducted to rank these factors in order of priority and provide the views of cost practitioners about the significance of these factors in the accurate estimation of LCC. The data from 138 construction building projects completed in UK were collected and analysed via multiple regression to discover the relationship between capital and LCCs and between non-cost factors and cost estimation at each stage of the life cycle (capital, operation, maintenance and LCC). Findings The results of analysis of existing LCC data of completing project and survey data from cost professionals are mostly consistent with many literature views and provide a reasonable description of the non-cost factors affecting the accuracy of estimates. Originality/value The value of this study is in the method used, which involves analysis of existing life data and survey data from cost professionals. The results provide a plausible description of the non-cost factors affecting the accuracy of estimates.

scholarly journals Multispecies Transcriptomics Data Set of Brugia malayi, Its Wolbachia Endosymbiont wBm, and Aedes aegypti across the B. malayi Life Cycle

2018 ◽  
Vol 7 (18) ◽  
Author(s):  
Matthew Chung ◽  
Laura Teigen ◽  
Silvia Libro ◽  
Robin E. Bromley ◽  
Nikhil Kumar ◽  
...  
Keyword(s):  
Life Cycle ◽  
Aedes Aegypti ◽  
Brugia Malayi ◽  
Adult Males ◽  
Data Set ◽  
Content Type ◽  
Transcriptomics Data ◽  
Wolbachia Endosymbiont ◽  
Males And Females ◽  
Stage 1

Here, we present a comprehensive transcriptomics data set of Brugia malayi, its Wolbachia endosymbiont wBm, and its vector host. This study samples from 16 stages across the entire B. malayi life cycle, including stage 1 through 4 larvae, adult males and females, embryos, immature microfilariae, and mature microfilariae.

Predicting the ground vibration induced by mine blasting using imperialist competitive algorithm

2018 ◽  
Vol 35 (4) ◽  
pp. 1774-1787 ◽  
Author(s):  
Katayoun Behzadafshar ◽  
Fahimeh Mohebbi ◽  
Mehran Soltani Tehrani ◽  
Mahdi Hasanipanah ◽  
Omid Tabrizi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ground Vibration ◽  
Imperialist Competitive Algorithm ◽  
Empirical Models ◽  
Coefficient Of Determination ◽  
Content Type ◽  
Competitive Algorithm ◽  
Spacing Ratio ◽  
Artificial Neural

PurposeThe purpose of this paper is to propose three imperialist competitive algorithm (ICA)-based models for predicting the blast-induced ground vibrations in Shur River dam region, Iran.Design/methodology/approachFor this aim, 76 data sets were used to establish the ICA-linear, ICA-power and ICA-quadratic models. For comparison aims, artificial neural network and empirical models were also developed. Burden to spacing ratio, distance between shot points and installed seismograph, stemming, powder factor and max charge per delay were used as the models’ input, and the peak particle velocity (PPV) parameter was used as the models’ output.FindingsAfter modeling, the various statistical evaluation criteria such as coefficient of determination (R2) were applied to choose the most precise model in predicting the PPV. The results indicate the ICA-based models proposed in the present study were more acceptable and reliable than the artificial neural network and empirical models. Moreover, ICA linear model with theR2 of 0.939 was the most precise model for predicting the PPV in the present study.Originality/valueIn the present paper, the authors have proposed three novel prediction methods based on ICA to predict the PPV. In the next step, we compared the performance of the proposed ICA-based models with the artificial neural network and empirical models. The results indicated that the ICA-based models proposed in the present paper were superior in terms of high accuracy and have the capacity to generalize.

scholarly journals A New Model for Predicting Rate of Penetration Using an Artificial Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2058 ◽  
Author(s):  
Salaheldin Elkatatny ◽  
Ahmed Al-AbdulJabbar ◽  
Khaled Abdelgawad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mutual Effect ◽  
Percentage Error ◽  
Drilling Process ◽  
Ann Model ◽  
Data Set ◽  
Individual Parameter ◽  
Rate Of Penetration ◽  
Artificial Neural

The drilling rate of penetration (ROP) is defined as the speed of drilling through rock under the bit. ROP is affected by different interconnected factors, which makes it very difficult to infer the mutual effect of each individual parameter. A robust ROP is required to understand the complexity of the drilling process. Therefore, an artificial neural network (ANN) is used to predict ROP and capture the effect of the changes in the drilling parameters. Field data (4525 points) from three vertical onshore wells drilled in the same formation using the same conventional bottom hole assembly were used to train, test, and validate the ANN model. Data from Well A (1528 points) were utilized to train and test the model with a 70/30 data ratio. Data from Well B and Well C were used to test the model. An empirical equation was derived based on the weights and biases of the optimized ANN model and compared with four ROP models using the data set of Well C. The developed ANN model accurately predicted the ROP with a correlation coefficient (R) of 0.94 and an average absolute percentage error (AAPE) of 8.6%. The developed ANN model outperformed four existing models with the lowest AAPE and highest R value.

Spatial effects and non-linearity in hedonic modeling

2018 ◽  
Vol 36 (1) ◽  
pp. 32-49 ◽  
Author(s):  
Marcelo Cajias ◽  
Sebastian Ertl
Keyword(s):  
Real Estate ◽  
Functional Form ◽  
Asymptotic Properties ◽  
Additive Model ◽  
Percentage Error ◽  
Spatial Effects ◽  
Hedonic Models ◽  
Data Set ◽  
Content Type ◽  
Out Of Sample

Purpose The purpose of this paper is to test the asymptotic properties and prediction accuracy of two innovative methods proposed along the hedonic debate: the geographically weighted regression (GWR) and the generalized additive model (GAM). Design/methodology/approach The authors assess the asymptotic properties of linear, spatial and non-linear hedonic models based on a very large data set in Germany. The employed functional form is based on the OLS, GWR and the GAM, while the estimation methodology was chosen to be iterative in forecasting, the fitted rents for each quarter based on their 1-quarter-prior functional form. The performance accuracy is measured by traditional indicators such as the error variance and the mean squared (percentage) error. Findings The results provide evidence for a clear disadvantage of the GWR model in out-of-sample forecasts. There exists a strong out-of-sample discrepancy between the GWR and the GAM models, whereas the simplicity of the OLS approach is not substantially outperformed by the GAM approach. Practical implications For policymakers, a more accurate knowledge on market dynamics via hedonic models leads to a more precise market control and to a better understanding of the local factors affecting current and future rents. For institutional researchers, instead, the findings are essential and might be used as a guide when valuing residential portfolios and forecasting cashflows. Even though this study analyses residential real estate, the results should be of interest to all forms of real estate investments. Originality/value Sample size is essential when deriving the asymptotic properties of hedonic models. Whit this study covering more than 570,000 observations, this study constitutes – to the authors’ knowledge – one of the largest data sets used for spatial real estate analysis.

Predicting likelihood of cost overrun in educational projects

2017 ◽  
Vol 24 (1) ◽  
pp. 21-39 ◽  
Author(s):  
Richard Ohene Asiedu ◽  
Nana Kena Frempong ◽  
Hans Wilhelm Alfen
Keyword(s):  
Linear Regression Analysis ◽  
Model Development ◽  
Multiple Linear Regression Analysis ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Cost Overrun ◽  
Cost Overruns ◽  
Content Type ◽  
Public Projects ◽  
Contract Signing

Purpose Being able to predict the likelihood of a project to overrun its cost before the contract signing phase is crucial in developing the required mitigating measures to avert it. Known parameters that permit the timely prediction of cost overrun provide the basis for such predictions. Therefore, the purpose of this paper is to develop a model for forecasting cost overruns. Design/methodology/approach Ten predictive variables known before the contract signing phase of a project are identified. Based on a survey approach, information on 321 educational projects completed are compiled. A multiple linear regression analysis is adopted for the model development. Findings Five variables – initial contract sum, gross floor area, number of storeys, source of funds and contractors’ financial classification are observed to influence cost overruns. The model, however, yields a fairly weak coefficient of determination with a mean absolute percentage error of 30.22 and 138 per cent, respectively. Research limitations/implications The model developed focussed on data only educational projects sampled from three out of the ten administration regions in Ghana based on a purposive sampling approach. Practical implications Policy makers and construction managers working on public projects stand to gain tremendous assistance in formulating and strengthening their own in-house cost forecasting at the precontract phase based on “what if” analysis to generate various alternative predictions of cost overruns. Originality/value Considering the innate nature of cost overruns within the Ghanaian construction industry often resulting to project abandonment, this research presents a unique dimension for tackling cost overruns based on a predictive approach.

Energy load forecasting: Bayesian and exponential smoothing hybrid methodology

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Eman Khorsheed
Keyword(s):  
Hybrid Approach ◽  
Peak Load ◽  
Exponential Smoothing ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Original Research ◽  
Model Parameters ◽  
Learning Tools ◽  
Content Type ◽  
Hybrid Methodology

Purpose The purpose of this study is to present a hybrid approach to model and predict long-term energy peak load using Bayesian and Holt–Winters (HW) exponential smoothing techniques. Design/methodology/approach Bayesian inference is administered by Markov chain Monte Carlo (MCMC) sampling techniques. Machine learning tools are used to calibrate the values of the HW model parameters. Hybridization is conducted to reduce modeling uncertainty. The technique is applied to real load data. Monthly peak load forecasts are calculated as weighted averages of HW and MCMC estimates. Mean absolute percentage error and the coefficient of determination (R2) indices are used to evaluate forecasts. Findings The developed hybrid methodology offers advantages over both individual combined techniques and reveals more accurate and impressive results with R2 above 0.97. The new technique can be used to assist energy networks in planning and implementing production projects that can ensure access to reliable and modern energy services to meet the sustainable development goal in this sector. Originality/value This is original research.

Boosted regression trees

2014 ◽  
Vol 19 (2) ◽  
pp. 152-167 ◽  
Author(s):  
William J. McCluskey ◽  
Dzurllkanian Zulkarnain Daud ◽  
Norhaya Kamarudin
Keyword(s):  
Real Estate ◽  
Regression Trees ◽  
Heterogeneous Data ◽  
Boosted Regression Trees ◽  
Categorical Variables ◽  
Percentage Error ◽  
Residential Property ◽  
Data Set ◽  
Content Type ◽  
Real Estate Valuation

Purpose – The purpose of this paper is to apply boosted regression trees (BRT) to a heterogeneous data set of residential property drawn from a jurisdiction in Malaysia, with the objective to evaluate its application within the mass appraisal environment in Malaysia. Machine learning (ML) techniques have been applied to real estate mass appraisal with varying degrees of success. Design/methodology/approach – To evaluate the performance of the BRT model two multiple regression analysis (MRA) models have been specified (linear and non-linear). One of the weaknesses of traditional regression is the need to a priori specify the functional form of the model and to ensure that all non-linearities have been accounted for. For a BRT model the algorithm does not require any predetermined model or variable transformations, making the process much simpler. Findings – The results show that the BRT model outperformed the MRA-specified models in terms of the coefficient of dispersion and mean absolute percentage error. While the results are encouraging, BRT models still lack transparency and suffer from the inability to translate variable importance into quantifiable variable effects. Practical implications – This paper presents a useful alternative modelling technique, BRT, for use within the mass appraisal environment in Malaysia. Its advantages include less intensive data cleansing, no requirement to specify the predictive underlying model, ability to utilise categorical variables without the need to transform them and not as data hungry, as for example, MRA. Originality/value – This paper adds to the knowledge in this area by applying a relatively new ML model, BRT to residential property data from a jurisdiction in Malaysia. BRT has shown promise as a strong predictive model when applied in other disciplines; therefore this research empirically tests this finding within real estate valuation.

Prediction of Fracture Force of Weld Line Based on Artificial Neural Networks

2011 ◽  
Vol 314-316 ◽  
pp. 547-553
Author(s):  
Peng Fei Zhu ◽  
Xiao Fang Sun ◽  
Ying Jun Lu ◽  
Hai Tian Pan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Process Parameters ◽  
Weld Line ◽  
Percentage Error ◽  
Maximum Relative Error ◽  
Melt Temperature ◽  
Data Set ◽  
Fracture Force ◽  
Artificial Neural

A feed-forward three-layer neural network was proposed to predict the fracture force of injection-molded parts’ weld line. Firstly, the most significant process parameters which affect the fracture force of weld line were analyzed. Secondly, melt temperature, injection pressure, holding pressure and holding time were chosen as import variables and the fracture force of weld line was chosen as output variable to construct artificial neural networks. Furthermore, the performance of ANN was evaluated and tested by its application to verification tests with process parameters randomly selected which all of them were not used in the network training. Results showed that the ANN predictions yield mean absolute percentage error (MAPE) in the range of 0.86％,and maximum relative error (MRE) in the range of 1.84％ for the test data set, and which can comparatively accurately reflect the influence relation of the injection process parameters on part’s quality index under the circumstance of data deficiencies.

scholarly journals Comparison of Accuracy of Artificial Neural Network (ANN) and Kriging Methods for Estimating Chlorine Concentration

10.29007/lpmh ◽  
2018 ◽  
Author(s):  
Faezeh Ghaleh Navi ◽  
Hamed Mazandarani Zadeh ◽  
Dragan Savic
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Kriging Model ◽  
Coefficient Of Determination ◽  
Monthly Basis ◽  
Data Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Observation Wells

Groundwater is one of the major sources of fresh water. Maintenance and management of this vital resource is so important especially in arid and semi-arid regions. Reliable and accurate groundwater quality assessment is essential as a basic data for any groundwater management studies. The aim of this study is to compare the accuracy of two Artificial Neural Network (ANN) and Kriging methods in predicting chlorine in groundwater. In case of ANN, we created an appropriate emulator, which minimize the prediction error by changing the parameters of the neural network, including the number of layers. The best Kriging model is also obtained by changing the variogram function, such that the Gaussian variogram has the least error in interpolation of the amount of chlorine. To evaluate the accuracy of these two methods, the mean square error (MSE) and Coefficient of determination (R2) are used. The data set consists of the amount of chlorine, in a monthly basis, measured at 112 observation wells from 1999 to 2015 in aquifer Qazvin, Iran. MSE values for ANN and Kriging are 14.8 and 15.4, respectively, which indicate that the ANN has a better performance and is more capable of predicting chlorine values in comparison with Kriging.

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