ESTIMATING THE RUNNING COSTS OF COMMERCIAL BUILDINGS: ARTIFICIAL NEURAL NETWORK MODELING

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Devindi Geekiyanage ◽  
Thanuja Ramachandra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Life Cycle Cost ◽  
Prediction Accuracy ◽  
Building Design ◽  
Commercial Buildings ◽  
Neural Network Modeling ◽  
Ann Model ◽  
Design Variables ◽  
Artificial Neural

Running costs of a building is a substantial share of its total life-cycle cost (LCC) and it ranges between 70-80% in commercial buildings. Despite its significant contribution to LCC, investors and construction industry practitioners tend to mostly rely on construction cost exclusively. Though the early stage estimation of running costs is limited due to the unavailability of historical cost data, several efforts have been taken to estimate the running costs of buildings using different cost estimation techniques. However, the prediction accuracy of those models is still challenged due to less quality and amount of data employed. This study, therefore, developed an artificial neural network (ANN) model for running costs estimation of commercial buildings with the use of building design variables. The study was quantitively approached and running costs data together with 13 building design variables were collected from 35 commercial buildings. The ANN model developed resulted in a 96.6% perfect correlation between the running cost and building design variables. The testing and validation of the model developed indicate that there is greater prediction accuracy. These findings will enable industry practitioners to make informed cost decisions on implications of running costs in commercial buildings at its early stages, eliminating excessive costs to be incurred during the operational phase.

scholarly journals Application of Wavelet Neural Network in Building Settlement Prediction

2020 ◽  
Vol 198 ◽  
pp. 03014
Author(s):  
Ruijie Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Building Design ◽  
Wavelet Neural Network ◽  
Deformation Monitoring ◽  
Polynomial Method ◽  
Observation Data ◽  
Bp Artificial Neural Network ◽  
Artificial Neural

Deformation monitoring, as a key link of information construction, runs through the entire process of the building design period, construction period and operation period[1]. At present, more mature static prediction methods include hyperbolic method, power polynomial method and Asaoka method. But these methods have many problems and shortcomings. In this paper, based on the characteristics of building foundation settlement and the methods widely discussed in this field, a wavelet neural network model with self-learning, self-organization and good nonlinear approximation ability is applied to the prediction problem of building settlement[2]. Using comparative analysis and induction method. The 20-phase monitoring data representing the deformation monitoring points of different settlement states of the line tunnel, using the observation data sequence of the first 15 phases respectively to take the cumulative settlement and interval settlement as training samples, through the BP artificial neural network and the improved wavelet neural network, for the last five periods Predict the observed settlement.Through the comparison, it is found that whether the interval settlement or the cumulative settlement is used, the prediction results of the wavelet neural network are basically better than the prediction results of the BP artificial neural network, and the number of trainings is greatly reduced. The adaptive prediction of the wavelet neural network. The ability is particularly obvious, and the prediction accuracy is significantly improved. Therefore, it can be shown that the wavelet neural network is indeed used in the settlement monitoring and forecast of buildings, which can obtain higher prediction accuracy and better prediction effect, and is a prediction method with great development potential.

scholarly journals Modeling SMEs’ Trust in the Implementation of Industry 4.0 using Kansei Engineering and Artificial Neural Network: Food and Beverage SMEs Context

2021 ◽  
Vol 53 (2) ◽  
Author(s):  
Mirwan Ushada ◽  
Titis Wijayanto ◽  
Fitri Trapsilawati ◽  
Tsuyoshi Okayama
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Industry 4.0 ◽  
Appropriate Technology ◽  
Preference Ranking ◽  
Neural Network Modeling ◽  
Kansei Engineering ◽  
Ann Model ◽  
Food And Beverage ◽  
Artificial Neural

Trust is an important aspect for policy makers in recommending the implementation of Industry 4.0 in food and beverage small and medium-sized enterprises (SMEs). SMEs’ trust in the implementation of Industry 4.0 is defined as the  level of belief in applying appropriate technology for Industry 4.0 based on their knowledge, familiarity, agreement and preference. Trust is a complex construct involving several Kansei words, or human mentality parameters. Artificial neural network modeling was utilized to model SMEs’ trust in implementation of Industry 4.0. The research objectives were: 1) to analyze the trust of SMEs in the implementation of Industry 4.0 using Kansei Engineering; 2) to model the trust of SMEs in the implementation of Industry 4.0 using an artificial neural network (ANN). A questionnaire was developed using Kansei words that were generated from adjectives to represent human mentality parameters, which were stimulated by visual samples of Industry 4.0 technology. The questionnaires were distributed among 190 respondents from the three large islands of Indonesia. The data were recapitulated for training, validating and testing the ANN model based on the backpropagation supervised learning method. The output was classification of trust as ‘distrust’, ‘trust’ or ‘overtrust’. The research results indicated that the SMEs’ trust was influenced by education, knowledge, familiarity, benefit, preference ranking and verbal components.

Mapping soil nail loads using Federal Highway Administration (FHWA) simplified models and artificial neural network technique

2020 ◽  
Vol 57 (10) ◽  
pp. 1453-1471 ◽  
Author(s):  
Peiyuan Lin ◽  
Pengpeng Ni ◽  
Chengchao Guo ◽  
Guoxiong Mei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Ann Model ◽  
Limit States ◽  
Operational Conditions ◽  
Soil Nail ◽  
Federal Highway ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This study compiles a broad database containing 312 measured maximum soil nail loads under operational conditions. The database is used to re-assess the prediction accuracies of the default Federal Highway Administration (FHWA) nail load model and its modified version previously reported in the literature. Predictions using the default and modified FHWA models are found to be highly dispersive. Moreover, the prediction accuracy is statistically dependent on the magnitudes of the predicted nail load and several model input parameters. The modified FHWA model is then recalibrated by introducing extra empirical terms to account for the influences of wall geometry, nail design configuration, and soil shear strength parameters on the evolvement of nail loads. The recalibrated FHWA model is demonstrated to have much better prediction accuracy compared to the default and modified models. Next, an artificial neural network (ANN) model is developed for mapping soil nail loads, which is shown to be the most advantageous one as it is accurate on average and the dispersion in prediction is low. The abovementioned dependency issue is also not present in the ANN model. The practical value of the ANN model is highlighted by applying it to reliability-based designs of soil nails against internal limit states.

scholarly journals Artificial Neural Network Modeling for Drug Dialyzability Prediction

2013 ◽  
Vol 16 (5) ◽  
pp. 665 ◽  
Author(s):  
Kahina Daheb ◽  
Mark L. Lipman ◽  
Patrice Hildgen ◽  
Julie J Roy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Protein Binding ◽  
Plasma Protein Binding ◽  
Plasma Protein ◽  
Neural Network Modeling ◽  
Ann Model ◽  
Drug Concentrations ◽  
Drug Removal ◽  
Artificial Neural

Purpose. The purpose of this study was to develop an artificial neural network (ANN) model to predict drug removal during dialysis based on drug properties and dialysis conditions. Nine antihypertensive drugs were chosen as model for this study. Methods. Drugs were dissolved in a physiologic buffer and dialysed in vitro in different dialysis conditions (UFRmin/UFRmax, with/without BSA). Samples were taken at regular intervals and frozen at -20ºC until analysis. Extraction methods were developed for drugs that were dialysed with BSA in the buffer.  Drug concentrations were quantified by high performance liquid chromatography (HPLC) or mass spectrometry (LC/MS/MS). Dialysis clearances (CLDs) were calculated using the obtained drug concentrations.  An ANOVA with Scheffe’s pairwise adjustments was performed on the collected data in order to investigate the impact of drug plasma protein binding and ultrafiltration rate (UFR) on CLD. The software Neurosolutions® was used to build ANNs that would be able to predict drug CLD (output). The inputs consisted of dialysis UFR and the herein drug properties: molecular weight (MW), logD and plasma protein binding. Results. Observed CLDs were very high for the majority of the drugs studied. The addition of BSA in the physiologic buffer statistically significantly decreased CLD for carvedilol (p= 0.002) and labetalol (p<0.001), but made no significant difference for atenolol (p= 0.100). In contrast, varying UFR does not significantly affect CLD (p>0.025). Multiple ANNs were built and compared, the best model was a Jordan and Elman network which showed learning stability and good predictive results (MSEtesting = 129). Conclusion. In this study, we have developed an ANN-model which is able to predict drug removal during dialysis. Since experimental determination of all existing drug CLDs is not realistic, ANNs represent a promising tool for the prediction of drug CLD using drug properties and dialysis conditions. This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.

scholarly journals Artificial Neural Network Prediction of Ischemic Tissue Fate in Acute Stroke Imaging

2010 ◽  
Vol 30 (9) ◽  
pp. 1661-1670 ◽  
Author(s):  
Shiliang Huang ◽  
Qiang Shen ◽  
Timothy Q Duong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acute Stroke ◽  
Prediction Accuracy ◽  
Ann Model ◽  
Characteristic Analysis ◽  
Stroke Treatment ◽  
Ischemic Tissue ◽  
Neighboring Pixel ◽  
Artificial Neural

Multimodal magnetic resonance imaging of acute stroke provides predictive value that can be used to guide stroke therapy. A flexible artificial neural network (ANN) algorithm was developed and applied to predict ischemic tissue fate on three stroke groups: 30-, 60-minute, and permanent middle cerebral artery occlusion in rats. Cerebral blood flow (CBF), apparent diffusion coefficient (ADC), and spin–spin relaxation time constant (T2) were acquired during the acute phase up to 3 hours and again at 24 hours followed by histology. Infarct was predicted on a pixel-by-pixel basis using only acute (30-minute) stroke data. In addition, neighboring pixel information and infarction incidence were also incorporated into the ANN model to improve prediction accuracy. Receiver-operating characteristic analysis was used to quantify prediction accuracy. The major findings were the following: (1) CBF alone poorly predicted the final infarct across three experimental groups; (2) ADC alone adequately predicted the infarct; (3) CBF+ADC improved the prediction accuracy; (4) inclusion of neighboring pixel information and infarction incidence further improved the prediction accuracy; and (5) prediction was more accurate for permanent occlusion, followed by 60- and 30-minute occlusion. The ANN predictive model could thus provide a flexible and objective framework for clinicians to evaluate stroke treatment options on an individual patient basis.

scholarly journals Artificial Neural Network Modeling of MoS2 Supercapacitor for Predicative Synthesis

2019 ◽  
Vol 9 (2) ◽  
pp. 554-560
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Storage ◽  
Neural Network Modeling ◽  
Ann Model ◽  
Energy Storage Devices ◽  
Storage Devices ◽  
Artificial Neural ◽  
Forward Calculation ◽  
Artificial Neural Network Ann

Energy storage systems are fundamental to the activity of intensity frameworks. They guarantee coherence of vitality supply and improve the dependability of the framework. The first area is centered on various energy storage frameworks, considering capacity limit, voltage and current proportions, and energy accessibility. Among the energy storage devices, supercapacitor is widely used because it is a high-limit capacitor with capacitance esteem a large amount than different capacitors. In the supercapacitor we have used MoS2 material synthesized with various Electrolytes. In perspective on the above mentioned, we report an Artificial Neural Network (ANN) strategy to achieve the predictable results. Levenberg- Marquardt feed-forward calculation prepares the neural network. We measure the exhibition of the ANN model with respect to mean square error (MSE) and the relationship coefficient between anticipated yield and yield given by the system. Results confirm the stability of supercapacitor over the other energy storage devices. To show such kind of conduct, we give Synthesis technique, Electrolyte, Cycle Life as an info esteems and Specific limit as yield esteem. For the amalgamation technique info esteem we have taken both compound and physical strategies by normalizing it. The practiced ANN demonstrating confirmations a higher number of concealed neuron design showing ideal execution as respects to expectation exactness

scholarly journals Optimization of Position and Number of Hotspot Detectors Using Artificial Neural Network and Genetic Algorithm to Estimate Material Levels Inside a Silo

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4427
Author(s):  
Jeong Hoon Rhee ◽  
Sang Il Kim ◽  
Kang Min Lee ◽  
Moon Kyum Kim ◽  
Yun Mook Lim
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Economic Feasibility ◽  
Ann Model ◽  
Ground Truth Data ◽  
Efficient Operation ◽  
Internal Storage ◽  
Artificial Neural

To realize efficient operation of a silo, level management of internal storage is crucial. In this study, to address the existing measurement limitations, a silo hotspot detector, which is typically utilized for internal silo temperature monitoring, was employed. The internal temperature data measured using the hotspot detectors were used to train an artificial neural network (ANN) algorithm to predict the level of the internal storage of the silo. The prediction accuracy was evaluated by comparing the predicted data with ground truth data. We combined the ANN model with the genetic algorithm (GA) to improve the prediction accuracy and establish efficient sensor installation positions and number to proceed with optimization. Simulation results demonstrated that the best predictive performance (up to 97% accuracy) was achieved when the ANN structure was 9-19-19-1. Furthermore, the numbers of efficient sensors and sensors positions determined using the proposed ANN-GA technique were reduced from seven to five or four, thereby ensuring economic feasibility.

scholarly journals An Empirical Comparison of Multiple Linear Regression and Artificial Neural Network for Concrete Dam Deformation Modelling

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Mingjun Li ◽  
Junxing Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Prediction Accuracy ◽  
Ann Model ◽  
Concrete Dam ◽  
Artificial Neural ◽  
Dam Deformation ◽  
Artificial Neural Network Ann

Deformation predicting models are essential for evaluating the health status of concrete dams. Nevertheless, the application of the conventional multiple linear regression model has been limited due to the particular structure, random loading, and strong nonlinear deformation of concrete dams. Conversely, the artificial neural network (ANN) model shows good adaptability to complex and highly nonlinear behaviors. This paper aims to evaluate the specific performance of the multiple linear regression (MLR) and artificial neural network (ANN) model in characterizing concrete dam deformation under environmental loads. In this study, four models, namely, the multiple linear regression (MLR), stepwise regression (SR), backpropagation (BP) neural network, and extreme learning machine (ELM) model, are employed to simulate dam deformation from two aspects: single measurement point and multiple measurement points, approximately 11 years of historical dam operation records. Results showed that the prediction accuracy of the multipoint model was higher than that of the single point model except the MLR model. Moreover, the prediction accuracy of the ELM model was always higher than the other three models. All discussions would be conducted in conjunction with a gravity dam study.

scholarly journals Estimation of unsteady hydromagnetic Williamson fluid flow in a radiative surface through numerical and artificial neural network modeling

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anum Shafiq ◽  
Andaç Batur Çolak ◽  
Tabassum Naz Sindhu ◽  
Qasem M. Al-Mdallal ◽  
T. Abdeljawad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Generation ◽  
Back Propagation ◽  
Neural Network Modeling ◽  
Multi Layer Perceptron ◽  
Ann Model ◽  
Feed Forward ◽  
Williamson Fluid ◽  
Artificial Neural

AbstractIn current investigation, a novel implementation of intelligent numerical computing solver based on multi-layer perceptron (MLP) feed-forward back-propagation artificial neural networks (ANN) with the Levenberg–Marquard algorithm is provided to interpret heat generation/absorption and radiation phenomenon in unsteady electrically conducting Williamson liquid flow along porous stretching surface. Heat phenomenon is investigated by taking convective boundary condition along with both velocity and thermal slip phenomena. The original nonlinear coupled PDEs representing the fluidic model are transformed to an analogous nonlinear ODEs system via incorporating appropriate transformations. A data set for proposed MLP-ANN is generated for various scenarios of fluidic model by variation of involved pertinent parameters via Galerkin weighted residual method (GWRM). In order to predict the (MLP) values, a multi-layer perceptron (MLP) artificial neural network (ANN) has been developed. There are 10 neurons in hidden layer of feed forward (FF) back propagation (BP) network model. The predictive performance of ANN model has been analyzed by comparing the results obtained from the ANN model using Levenberg-Marquard algorithm as the training algorithm with the target values. When the obtained Mean Square Error (MSE), Coefficient of Determination (R) and error rate values have been analyzed, it has been concluded that the ANN model can predict SFC and NN values with high accuracy. According to the findings of current analysis, ANN approach is accurate, effective and conveniently applicable for simulating the slip flow of Williamson fluid towards the stretching plate with heat generation/absorption. The obtained results showed that ANNs are an ideal tool that can be used to predict Skin Friction Coefficients and Nusselt Number values.

scholarly journals Modeling of Hydrogen Production by Applying Biomass Gasification: Artificial Neural Network Modeling Approach

Fermentation ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 71
Author(s):  
Sahar Safarian ◽  
Seyed Mohammad Ebrahimi Saryazdi ◽  
Runar Unnthorsson ◽  
Christiaan Richter
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Comprehensive Evaluation ◽  
Biomass Gasification ◽  
Proximate Analysis ◽  
Neural Network Modeling ◽  
Operating Parameters ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In order to accurately anticipate the proficiency of downdraft biomass gasification linked with a water–gas shift unit to produce biohydrogen, a model based on an artificial neural network (ANN) approach is established to estimate the specific mass flow rate of the biohydrogen output of the plant based on different types of biomasses and diverse operating parameters. The factors considered as inputs to the models are elemental and proximate analysis compositions as well as the operating parameters. The model structure includes one layer for input, a hidden layer and output layer. One thousand eight hundred samples derived from the simulation of 50 various feedstocks in different operating situations were utilized to train the developed ANN model. The established ANN in the case of product biohydrogen presents satisfactory agreement with input data: absolute fraction of variance (R2) is more than 0.999 and root mean square error (RMSE) is lower than 0.25. In addition, the relative impact of biomass properties and operating parameters on output are studied. At the end, to have a comprehensive evaluation, variations of the inputs regarding hydrogen-content are compared and evaluated together. The results show that almost all of the inputs show a significant impact on the smhydrogen output. Significantly, gasifier temperature, SBR, moisture content and hydrogen have the highest impacts on the smhydrogen with contributions of 19.96, 17.18, 15.3 and 10.48%, respectively. In addition, other variables in feed properties, like C, O, S and N present a range of 1.28–8.6% and proximate components like VM, FC and A present a range of 3.14–7.67% of impact on smhydrogen.

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