Construction cost estimation of reinforced and prestressed concrete bridges using machine learning

U ovom radu istraženo je sedam najnovijih postupaka strojnog učenja za procjenu troškova izgradnje armiranobetonskih i prednapetih betonskih mostova, uključujući umjetne neuronske mreže (ANN) i ansamble ANN, ansamble regresijskih stabala (eng. random forests, boosted and bagged regresijska stabla), metodu potpornih vektora za regresiju (SVR) i Gausov regresijski proces (GPR). Stvorena je i baza podataka o troškovima izgradnje i projektnim karakteristikama za 181 armiranobetonski i prednapeti betonski most za treniranje i ocjenu modela.Seven state-of-the-art machine learning techniques for estimation of construction costs of reinforced-concrete and prestressed concrete bridges are investigated in this paper, including artificial neural networks (ANN) and ensembles of ANNs, regression tree ensembles (random forests, boosted and bagged regression trees), support vector regression (SVR) method, and Gaussian process regression (GPR). A database of construction costs and design characteristics for 181 reinforced-concrete and prestressed-concrete bridges is created for model training and evaluation

Decision-Support System for Estimating Resource Consumption in Bridge Construction Based on Machine Learning

The paper presents and analyzes the state-of-the-art machine learning techniques that can be applied as a decision-support system in the estimation of resource consumption in the construction of reinforced concrete and prestressed concrete road bridges. The formed database on the consumption of concrete in the construction of bridges, along with their project characteristics, was the basis for the formation of the assessment model. The models were built using information from 181 reinforced concrete bridges in the eastern and southern branches of Corridor X in Serbia, with a value of more than 100 million euros. The application of artificial neural network models (ANNs), models based on regression trees (RTs), models based on support vector machines (SVM), and Gaussian processes regression (GPR) were analyzed. The accuracy of each model is determined by multi-criterion evaluation against four accuracy criteria root mean square error (RMSE), mean absolute error (MAE), Pearson’s linear correlation coefficient (R), and mean absolute percentage error (MAPE). According to all established criteria, the model based on GPR demonstrated the greatest accuracy in calculating the concrete consumption of bridges. According to the study, using automatic relevance determination (ARD) covariance functions results in the most accurate and optimal models and also makes it possible to see how important each input variable is to the model’s accuracy

Model for Forecasting and Assessment of Construction Cost of Reinforced-Concrete Bridges

U radu su predstavljene i analizirane najsavremenije tehnike mašinskog učenja koje se mogu primeniti kod procene troškova izgradnje armirano-betonskih drumskih mostova. Analizirana je primena veštačkih neuronskih mreža, ansambla regresionih stabala, modela zasnovanih na metodi potpornih vektora, Gausovih slučajnih procesa. Formirana baza podataka o troškovima izgradnje mostova zajedno sa njihovim projektnim karakteristikama predstavljala je osnovu za formiranje modela za procenu. Modeli su formirani na osnovu podataka za 181 armirano-betonski drumski most čija vrednost prevazilazi 100 miliona evra. Model zasnovan na metodi Gausovih procesa pokazao je najveću tačnost procene troškova izgradnje mostova. Istraživanje je ukazalo da primena ARD funkcija kovarijanse daje modele najveće tačnosti, a pored toga omogućava i sagledavanje značaja koje imaju pojedine ulazne promenljive na tačnost modela. Primenom modela sa ARD funkcijom kovarijanse formirani su i modeli za procenu utroška betona, visokovrednog i rebrastog čelika. Postignuta je tačnost modela kod procene ugovorenih troškova izgradnje izražena preko srednje apsolutne procentualne greške od 10,86%. Kod modela za procenu utroška ključnih materijala za izgradnju postignuta je tačnost modela čija je gornja granica 11,64% izražena preko srednje apsolutne procentualne greške. Sprovedeno istraživanje potvrđuje da je u ranim fazama razvoja projekta metodama baziranim na veštačkoj inteligenciji moguća brza i dovoljno precizna procena troškova izgradnje armirano-betonskih drumskih mostova i utroška ključnih materijala za njihovu gradnju.Contemporary machine learning techniques for assessment of construction costs of reinforced-concrete bridges, including artificial neural networks, regression tree ensembles, support vector regression and Gaussian random processes, are proposed and analysed in this dissertation. The database of construction costs and project characteristics is created, that served as a basis for building the assessment model. Data for 181 reinforced-concrete bridges were used in the database with the total value of over 100 000 000 EUR. The model based on Gaussian processes has shown the best performance in forecasting the construction costs of bridges. The results have proved that using the Automatic Relevance Determination (ARD) covariance function leads to the best prediction model, and moreover, it enables the assessment of the influence of input variables on the model performance. Models for the assessment of costs of concrete, as well as ribbed steel, were analysed. The mean absolute percentage error (MAPE) was used as the performance criterion. The best performing model gives MAPE equal to 10,86% for forecasting the contracted construction costs and MAPE equal to 11.64% for quantity estimation of the key construction materials. The research carried out in this dissertation confirms that the use of artificial intelligence based methods enables fast and accurate forecasting of construction costs of reinforced-concrete bridges, as well as the assessment of quantity estimation of the construction materials, even in early project phases

Application of artificial neural networks for hydrological modelling in Karst

The possibility of short-term water flow forecasting in a karst region is presented in this paper. Four state-of-the-art machine learning algorithms are used for the one day ahead forecasting: multi-layer perceptron neural network, radial basis function neural network, support vector machines for regression (SVR), and adaptive neuro fuzzy inference system (ANFIS). The results show that the ANFIS model outperforms other algorithms when the root mean square error and mean absolute error are used as quality indicators

Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Self-Compacting Concrete with Class F Fly Ash

Replacing a specified quantity of cement with Class F fly ash contributes to sustainable development and reducing the greenhouse effect. In order to use Class F fly ash in self-compacting concrete (SCC), a prediction model that will give a satisfactory accuracy value for the compressive strength of such concrete is required. This paper considers a number of machine learning models created on a dataset of 327 experimentally tested samples in order to create an optimal predictive model. The set of input variables for all models consists of seven input variables, among which six are constituent components of SCC, and the seventh model variable represents the age of the sample. Models based on regression trees (RTs), Gaussian process regression (GPR), support vector regression (SVR) and artificial neural networks (ANNs) are considered. The accuracy of individual models and ensemble models are analyzed. The research shows that the model with the highest accuracy is an ensemble of ANNs. This accuracy expressed through the mean absolute error (MAE) and correlation coefficient (R) criteria is 4.37 MPa and 0.96, respectively. This paper also compares the accuracy of individual prediction models and determines their accuracy. Compared to theindividual ANN model, the more transparent multi-gene genetic programming (MGPP) model and the individual regression tree (RT) model have comparable or better prediction accuracy. The accuracy of the MGGP and RT models expressed through the MAE and R criteria is 5.70 MPa and 0.93, and 6.64 MPa and 0.89, respectively

The influence of gamma radiation on polarization mode dispersion of fibers applied in communications

The fiber optics technology is constantly being developed, and is becoming an essential component of contemporary communications, medicine and industry. Fibers, their connections and system components play a major role in optical signal transmission, telecommunications, power transmission, and sensing processes using fiber technology. The two main light propagation characteristics of an optical fiber are attenuation and dispersion. The possibility of controling these parameters is of utmost importance for obtaining the requested transmission quality. This paper reports on an investigation to determine the influence of gamma radiation of 60Co on the variation of optical fiber propagation parameters, such as polarization mode dispersion. In addition, it also considers chosen topics in the field of fiber optics technology

Decision-Support System for Estimating Resource Consumption in Bridge Construction Based on Machine Learning

The paper presents and analyzes the state-of-the-art machine learning techniques that can be applied as a decision-support system in the estimation of resource consumption in the construction of reinforced concrete and prestressed concrete road bridges. The formed database on the consumption of concrete in the construction of bridges, along with their project characteristics, was the basis for the formation of the assessment model. The models were built using information from 181 reinforced concrete bridges in the eastern and southern branches of Corridor X in Serbia, with a value of more than 100 million euros. The application of artificial neural network models (ANNs), models based on regression trees (RTs), models based on support vector machines (SVM), and Gaussian processes regression (GPR) were analyzed. The accuracy of each model is determined by multi-criterion evaluation against four accuracy criteria root mean square error (RMSE), mean absolute error (MAE), Pearson’s linear correlation coefficient (R), and mean absolute percentage error (MAPE). According to all established criteria, the model based on GPR demonstrated the greatest accuracy in calculating the concrete consumption of bridges. According to the study, using automatic relevance determination (ARD) covariance functions results in the most accurate and optimal models and also makes it possible to see how important each input variable is to the model’s accuracy

Deep Learning of Quasar Lightcurves in the LSST Era

Deep learning techniques are required for the analysis of synoptic (multi-band and multi-epoch) light curves in massive data of quasars, as expected from the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). In this follow-up study, we introduce an upgraded version of a conditional neural process (CNP) embedded in a multi-step approach for the analysis of large data of quasars in the LSST Active Galactic Nuclei Scientific Collaboration data challenge database. We present a case study of a stratified set of u-band light curves for 283 quasars with very low variability ∼0.03. In this sample, the CNP average mean square error is found to be ∼5% (∼0.5 mag). Interestingly, besides similar levels of variability, there are indications that individual light curves show flare-like features. According to the preliminary structure–function analysis, these occurrences may be associated with microlensing events with larger time scales of 5–10 years