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Wyniki wyszukiwania dla: high-performance alkali-activated concrete compressive strength cost and carbon emission machine learning algorithms steel fiber
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Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete
PublikacjaHigh-performance alkali-activated concrete (HP-AAC) is acknowledged as a cementless and environmentally friendly material. It has recently received a substantial amount of interest not only due to the potential it has for being used instead of ordinary concrete but also owing to the concerns associated with climate change, sustainability, reduction of CO2 emissions, and energy consumption. The characteristics and amounts of the...
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Active learning on stacked machine learning techniques for predicting compressive strength of alkali-activated ultra-high-performance concrete
PublikacjaConventional ultra-high performance concrete (UHPC) has excellent development potential. However, a significant quantity of CO2 is produced throughout the cement-making process, which is in contrary to the current worldwide trend of lowering emissions and conserving energy, thus restricting the further advancement of UHPC. Considering climate change and sustainability concerns, cementless, eco-friendly, alkali-activated UHPC (AA-UHPC)...
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Data-driven Models for Predicting Compressive Strength of 3D-printed Fiber-Reinforced Concrete using Interpretable Machine Learning Algorithms
Publikacja3D printing technology is growing swiftly in the construction sector due to its numerous benefits, such as intricate designs, quicker construction, waste reduction, environmental friendliness, cost savings, and enhanced safety. Nevertheless, optimizing the concrete mix for 3D printing is a challenging task due to the numerous factors involved, requiring extensive experimentation. Therefore, this study used three machine learning...
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Data-Driven Modeling of Mechanical Properties of Fiber-Reinforced Concrete: A Critical Review
PublikacjaFiber-reinforced concrete (FRC) is extensively used in diverse structural engineering applications, and its mechanical properties are crucial for designing and evaluating its performance. The compressive, flexural, splitting tensile, and shear strengths of FRCs are among the most important attributes, which have been discussed more extensively than other properties. The accurate prediction of these properties, which are required...
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Machine-Learning Methods for Estimating Performance of Structural Concrete Members Reinforced with Fiber-Reinforced Polymers
PublikacjaIn recent years, fiber-reinforced polymers (FRP) in reinforced concrete (RC) members have gained significant attention due to their exceptional properties, including lightweight construction, high specific strength, and stiffness. These attributes have found application in structures, infrastructures, wind power equipment, and various advanced civil products. However, the production process and the extensive testing required for...
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Chained machine learning model for predicting load capacity and ductility of steel fiber–reinforced concrete beams
PublikacjaOne of the main issues associated with steel fiber–reinforced concrete (SFRC) beams is the ability to anticipate their flexural response. With a comprehensive grid search, several stacked models (i.e., chained, parallel) consisting of various machine learning (ML) algorithms and artificial neural networks (ANNs) were developed to predict the flexural response of SFRC beams. The flexural performance of SFRC beams under bending was...
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Machine Learning Techniques in Concrete Mix Design
PublikacjaConcrete mix design is a complex and multistage process in which we try to find the best composition of ingredients to create good performing concrete. In contemporary literature, as well as in state-of-the-art corporate practice, there are some methods of concrete mix design, from which the most popular are methods derived from The Three Equation Method. One of the most important features of concrete is compressive strength, which...
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Maturity curve for estimating the in-place strength of high performance concrete
PublikacjaThe paper presents the maturity curve for estimating the in-place early-age compressive strength of concrete. The development of appropriate maturity curve is a complex process. It is important to correctly determine the datum temperature and activation energy, which can be obtained in mortar tests. This paper describes an investigation of the accuracy of the maturity method to estimate the strength when different way to rate constant...
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Concrete mix design using machine learning
PublikacjaDesigning a concrete mix is a process of synthesizing many components, it is not a simple process and requires extensive technical knowledge. The design process itself focuses on obtaining the required strength of concrete. Very often designing a concrete mix takes into account the need to maintain the proper water-demand and frost-resistance features. The parameters that influence the concrete class most significantly are the...
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Machine learning-based prediction of preplaced aggregate concrete characteristics
PublikacjaPreplaced-Aggregate Concrete (PAC) is a type of preplaced concrete where coarse aggregate is placed in the mold and a Portland cement-sand grout with admixtures is injected to fill the voids. Due to the complex nature of PAC, many studies were conducted to determine the effects of admixtures and the compressive and tensile strengths of PAC. Considering that a prediction tool is needed to estimate the compressive and tensile...
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Machine learning-based prediction of preplaced aggregate concrete characteristics
PublikacjaPreplaced-Aggregate Concrete (PAC) is a type of preplaced concrete where coarse aggregate is placed in the mold and a Portland cement-sand grout with admixtures is injected to fill the voids. Due to the complex nature of PAC, many studies were conducted to determine the effects of admixtures and the compressive and tensile strengths of PAC. Considering that a prediction tool is needed to estimate the compressive and tensile strengths...
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Application of fiber optic sensors using Machine Learning algorithms for temperature measurement of lithium-ion batteries
PublikacjaOptical fiber sensorsusing low-coherence interferometry require processing ofthe output spectrum or interferogramto quickly and accurately determine the instantaneous value of the measured quantity, such as temperature.Methods based on machine learning are a good candidate for this application. The application of four such methods in an optical fiber temperature sensoris demonstrated.Using aZnO-coated...
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Model-Based Adaptive Machine Learning Approach in Concrete Mix Design
PublikacjaConcrete mix design is one of the most critical issues in concrete technology. This process aims to create a concrete mix which helps deliver concrete with desired features and quality. Contemporary requirements for concrete concern not only its structural properties, but also increasingly its production process and environmental friendliness, forcing concrete producers to use both chemically and technologically complex concrete...
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Concrete Compressive Strength Under Changing Environmental Conditions During Placement Processes
PublikacjaThe technological process of concrete production consists of several parts, including concrete mix design, concrete mix production, transportation of fresh concrete mix to a construction site, placement in concrete framework, and curing. Proper execution of these steps provides good quality concrete. Some factors can disturb the technological process, mainly temperature and excessive precipitation. Changing daily temperature and...
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Machine learning-based seismic response and performance assessment of reinforced concrete buildings
PublikacjaComplexity and unpredictability nature of earthquakes makes them unique external loads that there is no unique formula used for the prediction of seismic responses. Hence, this research aims to implement the most well-known Machine Learning (ML) methods in Python software to propose a prediction model for seismic response and performance assessment of Reinforced Concrete Moment-Resisting Frames (RC MRFs). To prepare 92,400 data...
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Computational Complexity and Its Influence on Predictive Capabilities of Machine Learning Models for Concrete Mix Design
PublikacjaThe design of concrete mixtures is crucial in concrete technology, aiming to produce concrete that meets specific quality and performance criteria. Modern standards require not only strength but also eco-friendliness and production efficiency. Based on the Three Equation Method, conventional mix design methods involve analytical and laboratory procedures but are insufficient for contemporary concrete technology, leading to overengineering...
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Robert Bogdanowicz prof. dr hab. inż.
OsobyRobert Bogdanowicz received his Ph.D. degree with honours in Electronics from the Gdansk University of Technology. He worked as a post-doc researcher in Ernst-Moritz-Arndt-Universität Greifswald Institut für Physik. He has initiated optical emission imaging of muti-magnetron pulsed plasma and contributed to the development of antibacterial implant coatings deposited by high-power impulse magnetron sputtering. He moved back to...
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The influence of the carbon equivalent on the weldability of high-strength low-alloy steel in the water environment
PublikacjaFrom many years, the high strength low alloy steels are often used for offshore constructions. This constructions, due to the environment in which they work, require more frequent repairs than the constructions from the land. For economic reasons, repairs take place in the underwater conditions, however water significantly decreases the weldability of steel. The paper presents the results of the CTS weldability test for S460ML...
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Grey wolf optimizer integrated within boosting algorithm: Application in mechanical properties prediction of ultra high-performance concrete including carbon nanotubes
PublikacjaNowadays, the construction industry has increasingly recognized the superior performance characteristics of ultra high-performance concrete (UHPC). Known for its exceptional durability and high tensile strength, UHPC material is revolutionizing structure standards subjected to extreme environmental conditions and heavy loads. This paper explores the enhancement of UHPC with nano- and micromaterials, employing advanced machine-learning...
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PERFORMANCE COMPARISON OF MACHINE LEARNING ALGORITHMS FOR PREDICTIVE MAINTENANCE
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High-Performance Machine-Learning-Based Calibration of Low-Cost Nitrogen Dioxide Sensor Using Environmental Parameter Differentials and Global Data Scaling
PublikacjaAccurate tracking of harmful gas concentrations is essential to swiftly and effectively execute measures that mitigate the risks linked to air pollution, specifically in reducing its impact on living conditions, the environment, and the economy. One such prevalent pollutant in urban settings is nitrogen dioxide (NO2), generated from the combustion of fossil fuels in car engines, commercial manufacturing, and food processing. Its...
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Acoustic emission signals in concrete beams under 3-point bending (plain concrete, steel fibre reinforced concrete, steel bar reinforced concrete)
Dane BadawczeThe DataSet contains the results of the mechanical behaviour of concrete beams with dimensions 40 x 40 x 160 cm3under the 3-point bending. Two concrete mixes, both based on the same design, were produced in the test programme. Mixture #1 was the plain concrete (PC), consisting of cement CEM I 42.5R (380 kg/m3), water (165 kg/m3), aggregate 0/2 mm (648...
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Weldability of high strength steel in underwater environment
PublikacjaThe article describes the problems with weldability of high-strength steels in the aquatic environment. The tendency of steel S355J2G3 and S500M to form cold cracks when welded in wet welding conditions has been experimentally evaluated. It was found that the tested steels have a high propensity to cracking. An experiment has been proposed and tested to evaluate the usefulness of the tempering bead technique as a method of improving...
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Robust and Efficient Machine Learning Algorithms for Visual Recognition
PublikacjaIn visual recognition, the task is to identify and localize all objects of interest in the input image. With the ubiquitous presence of visual data in modern days, the role of object recognition algorithms is becoming more significant than ever and ranges from autonomous driving to computer-aided diagnosis in medicine. Current models for visual recognition are dominated by models based on Convolutional Neural Networks (CNNs), which...
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Forecasting energy consumption and carbon dioxide emission of Vietnam by prognostic models based on explainable machine learning and time series
PublikacjaThis study assessed the usefulness of algorithms in estimating energy consumption and carbon dioxide emissions in Viet- nam, in which the training dataset was used to train the models linear regression, random forest, XGBoost, and AdaBoost, allowing them to comprehend the patterns and relationships between population, GDP, and carbon dioxide emissions, energy consumption. The results revealed that random forest, XGBoost, and AdaBoost...
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Comparative study on total nitrogen prediction in wastewater treatment 1 plant and effect of various feature selection methods on machine learning algorithms performance
PublikacjaWastewater characteristics prediction in wastewater treatment plants (WWTPs) is valuable and can reduce the number of sampling, energy, and cost. Feature Selection (FS) methods are used in the pre-processing section for enhancing the model performance. This study aims to evaluate the effect of seven different FS methods (filter, wrapper, and embedded methods) on enhancing the prediction accuracy for total nitrogen (TN) in the WWTP...
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Assessment of Failure Occurrence Rate for Concrete Machine Foundations Used in Gas and Oil Industry by Machine Learning
PublikacjaConcrete machine foundations are structures that transfer loads from machines in operation to the ground. The design of such foundations requires a careful analysis of the static and dynamic effects caused by machine exploitation. There are also other substantial differences between ordinary concrete foundations and machine foundations, of which the main one is that machine foundations are separated from the building structure....
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Underwater wet welding of S1300 ultra-high strength steel
PublikacjaMarine Structures Volume 81, January 2022, 103120 Underwater wet welding of S1300 ultra-high strength steel Author links open overlay panelJacekTomkówGrzegorzRogalski https://doi.org/10.1016/j.marstruc.2021.103120 Get rights and content Under a Creative Commons licenseopen access Highlights • Technological method of S1300 steel underwater weldability improving was proposed. • Number of cracks and hardness of welded joints was...
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Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods
PublikacjaNowadays, Buckling-Restrained Brace Frames (BRBFs) have been used as lateral force-resisting systems for low-, to mid-rise buildings. Residual Interstory Drift (RID) of BRBFs plays a key role in deciding to retrofit buildings after seismic excitation; however, existing formulas have limitations and cannot effectively help civil engineers, e.g., FEMA P-58, which is a conservative estimation method. Therefore, there is a need to...
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Acoustic emission signals in concrete beams under 3-point bending (polyolefin and steel fibre concrete)
Dane BadawczeThe DataSet contains the results of the mechanical behaviour of concrete beams with dimensions 40 x 40 x 160 cm3 under the 3-point bending. All specimens were manufactured based on the same concrete mixture composed of cement CEM I 42.5R (380 kg/m3), water (165 kg/m3), aggregate 0/2 mm (648 kg/m3), aggregate 2/8 mm (426 kg/m3), aggregate 8/16 mm (754...
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Prediction of fracture toughness in fibre-reinforced concrete, mortar, and rocks using various Machine learning techniques
PublikacjaMachine Learning (ML) method is widely used in engineering applications such as fracture mechanics. In this study, twenty different ML algorithms were employed and compared for the prediction of the fracture toughness and fracture load in modes I, II, and mixed-mode (I-II) of various materials, including fibre-reinforced concrete, cement mortar, sandstone, white travertine, marble, and granite. A set of 401 specimens of “Brazilian...
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Forewarned Is Forearmed: Machine Learning Algorithms for the Prediction of Catheter-Induced Coronary and Aortic Injuries
PublikacjaCatheter-induced dissections (CID) of coronary arteries and/or the aorta are among the most dangerous complications of percutaneous coronary procedures, yet the data on their risk factors are anecdotal. Logistic regression and five more advanced machine learning techniques were applied to determine the most significant predictors of dissection. Model performance comparison and feature importance ranking were evaluated. We identified...
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Machine learning-based prediction of seismic limit-state capacity of steel moment-resisting frames considering soil-structure interaction
PublikacjaRegarding the unpredictable and complex nature of seismic excitations, there is a need for vulnerability assessment of newly constructed or existing structures. Predicting the seismic limit-state capacity of steel Moment-Resisting Frames (MRFs) can help designers to have a preliminary estimation and improve their views about the seismic performance of the designed structure. This study improved data-driven decision techniques in...
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Machine learning for PhD students
Kursy OnlineAn introductory course in machine learning for PhD students from Department of Geotechnical and Hydraulic Engineering
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Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures
PublikacjaMany studies have been performed to put quantifying uncertainties into the seismic risk assessment of reinforced concrete (RC) buildings. This paper provides a risk-assessment support tool for purpose of retrofitting and potential design strategies of RC buildings. Machine Learning (ML) algorithms were developed in Python software by innovative methods of hyperparameter optimization, such as halving search, grid search, random...
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A Proposed Soft Computing Model for Ultimate Strength Estimation of FRP-Confined Concrete Cylinders
PublikacjaIn this paper, the feed-forward backpropagation neural network (FFBPNN) is used to propose a new formulation for predicting the compressive strength of fiber-reinforced polymer (FRP)-confined concrete cylinders. A set of experimental data has been considered in the analysis. The data include information about the dimensions of the concrete cylinders (diameter, length) and the total thickness of FRP layers, unconfined ultimate concrete...
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Effect of Base-Connection Strength and Ductility on the Seismic Performance of Steel Moment-Resisting Frames
PublikacjaColumn-base connections in steel moment-resisting frames (SMFs) in seismic regions are commonly designed to develop the capacity of adjoining column with an intent to develop a plastic hinge in the column member, rather than in the connection (i.e., a strong-base design). Recent research has shown base connections to possess high ductility, indicating that this practice may be not only expensive but also unnecessary. This suggests...
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Weldability of S460ML High Strength Low Alloy Steel in Underwater Conditions
PublikacjaThe paper presents experimental evaluation of susceptibility of the high strength S460ML steel to cold cracking in the conditions of wet welding with the use of covered electrodes. From the results of Tekken tests it was found out that the investigated steel was characterised, in the conditions of the carried out experiments (underwater wet welding and air welding with rutile electrodes), of high susceptibility to cold cracking....
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Cold Cracking Of Underwater Wet Welded S355G10+N High Strength Steel
PublikacjaWater as the welding environment determines some essential problems influencing steel weldability. Underwater welding of high strength steel joints causes increase susceptibility to cold cracking, which is an effect of much faster heat transfer from the weld area and presence of diffusible hydrogen causing increased metal fragility. The paper evaluates the susceptibility to cold cracking of the high strength S355G10+N steel used,...
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Correlation between Compressive Strength and Heat of Hydration of Cement Mortars with Siliceous Fly Ash
PublikacjaThis paper presents the results of calorimetric and strength tests of mortars with ordinary Portland cement and two substitution rates (10 and 20%) of cement by siliceous fly ash. The prepared samples were cured under isothermal conditions at four different temperatures: 23, 33, 43 and 53 °C. Heat of hydration was measured using an isothermal calorimeter dedicated to monitor the hydration process of cementitious composites such...
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Strength testing machine results
Dane BadawczeThe aim of the research project is to determine the rotational stiffness of the connection between the purlin and the part of the truss top chord. The attached files are referred to the forces and displacements obtained from the strength testing machine (two sensors). The 16 specimens were tested on the experimental set up.
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Fire-induced spalling of ultra-high performance concrete: A systematic critical review
PublikacjaUltra-high performance concrete (UHPC) is a novel concrete class characterized by a compressive strength of more than 150 MPa. One of the most significant drawbacks of employing UHPC is that is very low permeability owing to its great compactness of dense structure increases the risk of fire-induced spalling. It is challenging for fire safety and structural engineers to predict and analyze this issue due to the lack of widely accepted...
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A Review on Machine Learning Deployment Patterns and Key Features in the Prediction of Preeclampsia
PublikacjaPrevious reviews have investigated machine learning (ML) models used to predict the risk of developing preeclampsia. However, they have not addressed the intended deployment of these models throughout pregnancy, nor have they detailed feature performance. This study aims to provide an overview of existing ML models and their intended deployment patterns and performance, along with identified features of high importance. This review...
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Comparative study on fracture evolution in steel fibre and bar reinforced concrete beams using acoustic emission and digital image correlation techniques
PublikacjaIn recent decades, the demand for sustainable construction practices has increased, but raw materials such as reinforcing steel remain scarce. Therefore, steel fibres have emerged as a popular and sustainable choice in the construction industry, offering a cost-effective alternative to traditional steel bar reinforcement for both flatwork and elevated structures. The purpose of this study is therefore to compare the performance...
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Influence of Heat Treatment Temperature on Fatigue Toughness in Medium-Carbon High-Strength Steels
PublikacjaCurrent research has demonstrated that the tempering temperature affects the martensitic transformation of medium-carbon high-strength steels. This temperature plays an important role in the final microstructure, percentage ratios of martensite to ferrite phases and, consequently, in the mechanical properties and the fatigue response. So far, the relationship between the martensitic tempering temperature and the cyclic deformation...
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Machine Learning in Multi-Agent Systems using Associative Arrays
PublikacjaIn this paper, a new machine learning algorithm for multi-agent systems is introduced. The algorithm is based on associative arrays, thus it becomes less complex and more efficient substitute of artificial neural networks and Bayesian networks, which is confirmed by performance measurements. Implementation of machine learning algorithm in multi-agent system for aided design of selected control systems allowed to improve the performance...
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Ultimate compressive strength assessment of uncleaned and cleaned corroded plates with locked crack
PublikacjaThe work presented here investigates the structural response of cleaned corroded plates, subjected to compressive load in the presence of a locked crack, where the change of mechanical properties as a result of corrosion development and the cleaning process is also accounted for. A Finite Element model for assessing the compressive strength, considering geometric and material nonlinearities, is developed, and the analysed plates...
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Optimization-based stacked machine-learning method for seismic probability and risk assessment of reinforced concrete shear walls
PublikacjaEfficient seismic risk assessment aids decision-makers in formulating citywide risk mitigation plans, providing insights into building performance and retrofitting costs. The complexity of modeling, analysis, and post-processing of the results makes it hard to fast-track the seismic probabilities, and there is a need to optimize the computational time. This research addresses seismic probability and risk assessment of reinforced...
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Statistical Data Pre-Processing and Time Series Incorporation for High-Efficacy Calibration of Low-Cost NO2 Sensor Using Machine Learning
PublikacjaAir pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO2), among these harmful gases, is notably prevalent in densely populated urban regions. Given...
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