scholarly journals Accident Modeling in Small-scale Construction Projects Based on Artificial Neural Networks

2019 ◽  
Vol 5 (3) ◽  
pp. 121-126
Author(s):  
Behrouz Alizadeh Savareh ◽  
Mohsen Mahdinia ◽  
Samira Ghiyasi ◽  
Jamshid Rahimi ◽  
Ahmad Soltanzadeh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Construction Projects ◽  
Scale Construction ◽  
Small Scale ◽  
Artificial Neural

Active Absorption of Random Waves in Wave Flume Using Artificial Neural Networks

2020 ◽  
Author(s):  
Áureo I. W. Ramos ◽  
Antonio C. Fernandes ◽  
Vanessa M. Thomaz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Control Algorithm ◽  
Wave Generation ◽  
Reverse Direction ◽  
Small Scale ◽  
Wave Absorption ◽  
Wave Flume ◽  
Wave Channel ◽  
Artificial Neural

Abstract A wave flume is primarily intended to reproduce actual sea conditions in order to provide a reliable means of testing for small scale models. The realization of scaled tests is extremely important for the validation of a project in real scale, since, through the laws of similitude, such tests make it possible to predict the behavior of structures in the ocean as well as their performance during operation. This research aims to develop, test and validate an active control algorithm for wave absorption in a 2D wave channel — that is, when the waves propagate in only one direction — based on artificial neural networks (ANN). The ANN control algorithm relies on the linear wave theory and the principle of time reversal of wave propagation, i.e. the phenomenon of wave absorption corresponds to the wave generation when observed in the reverse direction of time. Through this principle, data from wave generation experiments, after proper manipulation, are used to train an ANN capable of generating the control signal used to move the wave generator device, this time as a wave absorber.

scholarly journals A method for mobile device positioning using a sensor network of BLE beacons, approximation of the RSSI value and artificial neural networks

2021 ◽  
Vol 45 (2) ◽  
pp. 277-285
Author(s):  
A.V. Astafiev ◽  
D.V. Titov ◽  
A.L. Zhiznyakov ◽  
A.A. Demidov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sensor Network ◽  
Mobile Device ◽  
Experimental Studies ◽  
Test Sample ◽  
Small Scale ◽  
Approximation Function ◽  
Artificial Neural

The paper considers the development of a method for positioning a mobile device using a sensor network of BLE-beacons, the approximation of RSSI values and artificial neural networks. The aim of the work is to develop a method for positioning small-scale industrial mechanization equipment for building unmanned systems for product movement tracking. The work is divided into four main parts: data synthesis, signal filtering, selection of BLE beacons, translation of the RSSI values into a distance, and multilateration. A simplified Kalman filter is proposed for filtering the input signal to suppress Gaussian noise. A description of two approaches to translating the RSSI value into a distance is given: an exponential approximation function with a coefficient of determination of 0.6994 and an artificial feedforward neural network. A comparison of the results of these approaches is carried out on several test samples: a training one, a test sample at a known distance (0–50 meters) and a test sample at an unknown distance (60–100 meters). The artificial neural network is shown to perform better in all experiments, except for the test sample at a known distance (0–50 meters), for which the r.m.s. error is higher by 0.02 m 2 than that for the approximation function, which can be neglected. An algorithm for positioning a mobile device based on the multilateration method is proposed. Experimental studies of the developed method have shown that the positioning error does not exceed 0.9 meters in a 5×5.5 m room under monitoring. The positioning accuracy of a mobile device using the proposed method in the experiment is 40.9 % higher. Experimental studies are also conducted in a 58.4×4.5 m room, showing more accurate results compared to similar studies.

scholarly journals Artificial neural networks incorporating cost significant Items towards enhancing estimation for (life-cycle) costing of construction projects

2013 ◽  
Vol 13 (3) ◽  
pp. 51-64 ◽  
Author(s):  
Ayedh Alqahtani ◽  
Andrew Whyte
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Life Cycle ◽  
Life Cycle Cost ◽  
Construction Projects ◽  
Back Propagation ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Excel Solver

Industrial application of life-cycle cost analysis (LCCA) is somewhat limited, with techniques deemed overly theoretical, resulting in a reluctance to realise (and pass onto the client) the advantages to be gained from objective (LCCA) comparison of (sub)component material specifications. To address the need for a user-friendly structured approach to facilitate complex processing, the work described here develops a new, accessible framework for LCCA of construction projects; it acknowledges Artificial Neural Networks (ANNs) to compute the whole-cost(s) of construction and uses the concept of cost significant items (CSI) to identify the main cost factors affecting the accuracy of estimation. ANNs is a powerful means to handle non-linear problems and subsequently map between complex input/output data, address uncertainties. A case study documenting 20 building projects was used to test the framework and estimate total running costs accurately. Two methods were used to develop a neural network model; firstly a back-propagation method was adopted (using MATLAB SOFTWARE); and secondly, spread-sheet optimisation was conducted (using Microsoft Excel Solver). The best network was established as consisting of 19 hidden nodes, with the tangent sigmoid used as a transfer function of NNs model for both methods. The results find that in both neural network models, the accuracy of the developed NNs model is 1% (via Excel-solver) and 2% (via back-propagation) respectively.

scholarly journals Flooding probability of urban area estimated by decision tree and artificial neural networks

2008 ◽  
Vol 10 (1) ◽  
pp. 57-67 ◽  
Author(s):  
Jeng-Chung Chen ◽  
Ching-Sung Shu ◽  
Shu-Kuang Ning ◽  
Ho-Wen Chen
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Small Scale ◽  
Urban Drainage ◽  
Rainfall Events ◽  
Second Stage ◽  
Downtown Area ◽  
Observation Network ◽  
Artificial Neural ◽  
Flooding Probability

Remote sensing, such as from satellite, has been recognized as useful for monitoring the changes in hydrology. In this study, we propose a way that is able to estimate flooding probability based on satellite data from the observation network of the World Meteorological Organization. Through a two-stage probability analysis, we can depict the area with high flooding potential in near-real time. In the first stage, decision trees offered a prompt and rough estimation of the flooding probability; in the second stage, artificial neural networks handle the rainfall forecast in a small-scale area. Case studies, simulating two rainfall events on 20 May 2004 and 11 July 2001, proved that our proposed method is promising for mitigating the flooding damage along urban drainage within the downtown area of Kaohsiung city.

scholarly journals ANN Based Approach for Estimation of Construction Costs of Sports Fields

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Michał Juszczyk ◽  
Agnieszka Leśniak ◽  
Krzysztof Zima
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cost Estimation ◽  
Construction Projects ◽  
Construction Costs ◽  
General Applicability ◽  
Artificial Neural ◽  
Multilayer Perceptron Networks ◽  
Cost Of Construction ◽  
Sports Fields

Cost estimates are essential for the success of construction projects. Neural networks, as the tools of artificial intelligence, offer a significant potential in this field. Applying neural networks, however, requires respective studies due to the specifics of different kinds of facilities. This paper presents the proposal of an approach to the estimation of construction costs of sports fields which is based on neural networks. The general applicability of artificial neural networks in the formulated problem with cost estimation is investigated. An applicability of multilayer perceptron networks is confirmed by the results of the initial training of a set of various artificial neural networks. Moreover, one network was tailored for mapping a relationship between the total cost of construction works and the selected cost predictors which are characteristic of sports fields. Its prediction quality and accuracy were assessed positively. The research results legitimatize the proposed approach.

scholarly journals A simple formulation for early-stage cost estimation of building construction projects

2021 ◽  
Vol 73 (08) ◽  
pp. 819-832
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Construction Projects ◽  
Early Stage ◽  
Gene Expression Programming ◽  
Cost Calculation ◽  
Data Set ◽  
Algorithm Configuration ◽  
Artificial Neural

This study is aimed at improving a formula that enables easy, correct, and fast estimation of an Early-Stage Cost of Buildings (ESCE). This formula, enabling estimation of ESCE, was developed by the authors based on artificial neural networks and gene expression programming. A quantity survey was conducted for a hundred construction projects, and a data set was created. This data set was analysed with many Artificial Neural Networks to determine the variables that affect ESCE. An algorithm configuration was made with Gene Expression Programming, and the ESCE formula was created using this algorithm configuration. This formula estimates ESCE with satisfactory precision. The use of the proposed formula in the early-stage building cost calculations is important not only for faster and easier cost calculation but also to prevent any differences that may arise due to the individual making the calculations.

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