Search Results (13)

In this study, the city of Banja Luka is used as a case study to evaluate horizontal PGA values in regions with a history of moderate to strong earthquakes and with different deep geological conditions. We present regional attenuation equations for PGA that can capture both the impacts of deep geology and local soil conditions. A PSHA study for a site in Banja Luka was carried out using the developed empirical scaling equations and compared to all previous seismic hazard estimations for the same region. The data indicate that variations in deep geological conditions may have a greater impact on PGA values than local soil effects. Given the scarcity of scaling equations that consider deep geology in addition to local soil conditions, we believe this case study is a step toward developing more accurate PGA estimates for comparable regions. Full article

Vertical PGA is frequently included in civil engineering regulations simply by multiplying the horizontal PGA by a constant. Moreover, most design codes, including Eurocode 8, do not consider the impact of the local soil on vertical ground motion at all. In this study, we demonstrate that such practices increase earthquake risks. The article examines vertical PGA strong-motion estimations for the city of Banja Luka. Banja Luka serves as a case study for areas with records of moderate to strong earthquakes and diverse deep geological conditions. Regional equations for scaling vertical PGA are presented. The vertical PGA values and vertical to horizontal PGA ratios are calculated and analyzed. The findings indicate that the vertical to horizontal PGA ratios for the rock sites depend on the source-to-site distance and deep geology and fall between 0.30 and 0.66. Hence, these ratios cannot be approximated by a single value of 0.90 and 0.45, as specified by Eurocode 8 for Type 1 and Type 2 spectra, respectively. Moreover, the results show that the deep geology effects on vertical ground motion can exceed the local soil effects. When the amount of recorded data from comparable areas increases, we will be able to properly calibrate the existing scaling equations and obtain more reliable estimates of vertical PGA. Full article

This study presents a detailed analysis of various machine learning models for predicting the interfacial bond strength of fiber-reinforced polymer (FRP) concrete, including multiple linear regression, Multigene Genetic Programming (MGGP), an ensemble of regression trees, Gaussian Process Regression (GPR), Support Vector Regression (SVR), and neural networks. The evaluation was based on their predictive accuracy. The optimal model identified was the GPR ARD Exponential model, which achieved a mean absolute error (MAE) of 1.8953 MPa and a correlation coefficient (R) of 0.9658. An analysis of this optimal model highlighted the most influential variables affecting the bond strength. Additionally, the research identified several models with lower expression complexity and reduced accuracy, which may still be applicable in practical scenarios. Full article

The construction industry faces the challenge of conserving natural resources while maintaining environmental sustainability. This study investigates the feasibility of using recycled materials, particularly crushed clay bricks, as replacements for conventional aggregates in concrete. The research aims to optimize the performance of both single regression tree models and ensembles of regression trees in predicting concrete properties. The study focuses on optimizing key parameters like the minimum leaf size in the models. By testing various minimum leaf sizes and ensemble methods such as Random Forest and TreeBagger, the study evaluates metrics including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The analysis indicates that the most influential factors on concrete characteristics are the concrete’s age, the amount of superplasticizer used, and the size of crushed brick particles exceeding 4 mm. Additionally, the water-to-cement ratio significantly impacts the predictions. The regression tree models showed optimal performance with a minimum leaf size, achieving an RMSE of 4.00, an MAE of 2.95, an MAPE of 0.10, and an R² of 0.96. Full article

To promote sustainable growth and minimize the greenhouse effect, rice husk fly ash can be used instead of a certain amount of cement. The research models the effects of using rice fly ash as a substitute for regular Portland cement on the compressive strength of concrete. In this study, different machine-learning techniques are investigated and a procedure to determine the optimal model is provided. A database of 909 analyzed samples forms the basis for creating forecast models. The derived models are assessed using the accuracy criteria RMSE, MAE, MAPE, and R. The research shows that artificial intelligence techniques can be used to model the compressive strength of concrete with acceptable accuracy. It is also possible to evaluate the importance of specific input variables and their influence on the strength of such concrete. Full article

This research delves into the efficacy of machine learning models in predicting water quality parameters within a catchment area, focusing on unraveling the significance of individual input variables. In order to manage water quality, it is necessary to determine the relationship between the physical attributes of the catchment, such as geological permeability and hydrologic soil groups, and in-stream water quality parameters. Water quality data were acquired from the Iran Water Resource Management Company (WRMC) through monthly sampling. For statistical analysis, the study utilized 5-year means (1998–2002) of water quality data. A total of 88 final stations were included in the analysis. Using machine learning methods, the paper gives relations for 11 in-stream water quality parameters: Sodium Adsorption Ratio (SAR), Na⁺, Mg²⁺, Ca²⁺, SO₄²⁻, Cl⁻, HCO³⁻, K⁺, pH, conductivity (EC), and Total Dissolved Solids (TDS). To comprehensively evaluate model performance, the study employs diverse metrics, including Pearson’s Linear Correlation Coefficient (R) and the mean absolute percentage error (MAPE). Notably, the Random Forest (RF) model emerges as the standout model across various water parameters. Integrating research outcomes enables targeted strategies for fostering environmental sustainability, contributing to the broader goal of cultivating resilient water ecosystems. As a practical pathway toward achieving a delicate balance between human activities and environmental preservation, this research actively contributes to sustainable water ecosystems. Full article

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. Full article

Accurate prediction of the prestressed steel amount is essential for a concrete-road bridge’s successful design, construction, and long-term performance. Predicting the amount of steel required can help optimize the design and construction process, and also help project managers and engineers estimate the overall cost of the project more accurately. The prediction model was developed using data from 74 constructed bridges along Serbia’s Corridor X. The study examined operationally applicable models that do not require indepth modeling expertise to be used in practice. Neural networks (NN) models based on regression trees (RT) and genetic programming (GP) models were analyzed. In this work, for the first time, the method of multicriteria compromise ranking was applied to find the optimal model for the prediction of prestressed steel in prestressed concrete bridges. The optival model based on GP was determined using the VIKOR method of multicriteria optimization; the accuracy of which is expressed through the MAPE criterion is 9.16%. A significant average share of 46.11% of the costs related to steelworks, in relation to the total costs, indicates that the model developed in the paper can also be used for the implicit estimation of construction costs. Full article

Buildings can be made more user-friendly and secure by putting “smart” design strategies and technology processes in place. Such strategies and processes increase energy efficiency, make it possible to use resources rationally, and lower maintenance and construction costs. In addition to using wireless technologies and sensors to improve thermal, visual, and acoustic comfort, “smart” buildings are known for their energy, materials, water, and land management systems. Smart buildings use wireless technologies and sensors to improve thermal, visual, and acoustic comfort. These systems are known for managing energy, materials, water, and land. The task of the study is to consider the indicators that form the basis of the framework for evaluating intelligent buildings. The indicators for the development of “smart” buildings are classified into six categories in this paper: green building construction, energy management systems, safety and security management systems, occupant comfort and health, building automation and control management systems, and communication and data sharing. The paper aims to develop a scoring model for the smartness of public buildings. In developing the scoring system, the decision-making process requires an appropriate selection of the optimal solution. The contents of the research are the methods known as the Pythagorean Fuzzy Analytic Hierarchy Process (PF-AHP), Interval Valued Pythagorean Fuzzy AHP with differences (IVPF-AHP d), and the proposed method Interval Valued Pythagorean Fuzzy AHP (IVPF-AHP p). The research focuses on the IVPF-AHP as one of the methods of Multi-Criteria Decision-Making (MCDM) and its implementation. The comparative analysis of the three presented methods indicates a significant degree of similarity in the ranking, which confirms the ranking similarity. The results highlight the importance of bioclimatic design, smart metering, ecological materials, and renewable energy systems. Full article

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. Full article

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. Full article

In current practice, the remediation of landslides has shown that the biggest problem is the increase in the number of works, and therefore the price of the works. This is due to several factors, including characteristic of the soil, such as the collapse (collapse) of the surrounding ground around the main slide during landslide remediation. Unless these soil erosion effects are taken into account, recovery costs will overrun, which can jeopardize the planned budget. This paper presents a multi-criteria optimization of landslide remediation using the PROMETHEE method and determines the optional number of walls for the additional soil erosion. In a case study on examples of real landslides in the Republic of Serbia, the application of the method is presented and appropriate conclusions are drawn. Full article

This paper gives a comprehensive overview of the state-of-the-art machine learning methods that can be used for estimating self-compacting rubberized concrete (SCRC) compressive strength, including multilayered perceptron artificial neural network (MLP-ANN), ensembles of MLP-ANNs, regression tree ensembles (random forests, boosted and bagged regression trees), support vector regression (SVR) and Gaussian process regression (GPR). As a basis for the development of the forecast model, a database was obtained from an experimental study containing a total of 166 samples of SCRC. Ensembles of MLP-ANNs showed the best performance in forecasting with a mean absolute error (MAE) of 2.81 MPa and Pearson’s linear correlation coefficient (R) of 0.96. The significantly simpler GPR model had almost the same accuracy criterion values as the most accurate model; furthermore, feature reduction is easy to combine with GPR using automatic relevance determination (ARD), leading to models with better performance and lower complexity. Full article