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Farzin Kazemi
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Catalog Publications
Year 2025
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Active learning on stacked machine learning techniques for predicting compressive strength of alkali-activated ultra-high-performance concrete
PublicationConventional 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)...
Year 2024
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Active Learning on Ensemble Machine-Learning Model to Retrofit Buildings Under Seismic Mainshock-Aftershock Sequence
PublicationThis research presents an efficient computational method for retrofitting of buildings by employing an active learning-based ensemble machine learning (AL-Ensemble ML) approach developed in OpenSees, Python and MATLAB. The results of the study shows that the AL-Ensemble ML model provides the most accurate estimations of interstory drift (ID) and residual interstory drift (RID) for steel structures using a dataset of 2-, to 9-story...
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Chained machine learning model for predicting load capacity and ductility of steel fiber–reinforced concrete beams
PublicationOne 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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Computational Bar Size Optimization of Single Layer Dome Structures Considering Axial Stress and Shape Disturbance
PublicationA computational method is proposed in this paper to minimize the material usage in the construction of modern spatial frame structures by prestressing a minimal number of members. The computational optimization is conducted in two steps. Firstly, a numerical model of a single-layer dome structure is used to minimize the cross-sectional area through several iterations. Different assumed ratios (r) ranging from 0.95 to 0.75 are multiplied...
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Data-Driven Modeling of Mechanical Properties of Fiber-Reinforced Concrete: A Critical Review
PublicationFiber-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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Development and experimental validation of a novel double-stage yield steel slit damper-buckling restrained brace
PublicationThis research is focused on the development and experimental validation of a novel double-stage yield steel slit damper-buckling restrained brace (SSD-DYB) system designed for seismic resistance of steel structures. The SSD-DYB integrates the energy dissipation capability of a steel slit damper (SSD) in its initial segment, enhancing performance in the case of lower seismic intensities levels while employing a larger segment for...
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Enhancing seismic performance of steel buildings having semi-rigid connection with infill masonry walls considering soil type effects
PublicationUnpreventable constructional defects are the main issues in the case of steel Moment-Resisting Frames (MRFs) that mostly occur in the rigidities of beam-to-column connections. The present article aims to investigate the effects of different rigidities of structures and to propose Infill Masonry Walls (IMWs) as retrofitting strategy for the steel damaged buildings. A fault or failure to meet a certain consideration of the soil type...
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Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete
PublicationHigh-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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Machine-Learning Methods for Estimating Performance of Structural Concrete Members Reinforced with Fiber-Reinforced Polymers
PublicationIn 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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Optimization-based stacked machine-learning method for seismic probability and risk assessment of reinforced concrete shear walls
PublicationEfficient 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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Seismic probabilistic assessment of steel and reinforced concrete structures including earthquake-induced pounding
PublicationRecent earthquakes demonstrate that prioritizing the retrofitting of buildings should be of the utmost importance for enhancing the seismic resilience and structural integrity of urban structures. To have a realistic results of the pounding effects in modeling process of retrofitting buildings, the present research provides seismic Probability Factors (PFs), which can be used for estimating collision effects without engaging in...
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Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods
PublicationNowadays, 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...
Year 2023
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Advanced Scalar-valued Intensity Measures for Residual Drift Prediction of SMRFs with Fluid Viscous Dampers
PublicationMaximum Residual Inter-story Drift Ratio (RIDRmax) plays an important role to specify the state of a structure after severe earthquake and the possibility of repairing the structure. Therefore, it is necessary to predict the RIDRmax of Steel Moment-Resisting Frames (SMRFs) with high reliability by employing powerful Intensity Measures (IMs). This study investigates the efficiency and sufficiency of scalar-valued IMs for predicting...
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Enhancing seismic performance of buckling-restrained brace frames equipped with innovative bracing systems
PublicationNowadays, to improve the performance of conventional bracing systems, in which, buckling in the pressure loads is the main disadvantage, the buckling-restrained brace (BRB) is introduced as a solution. In this study, the performance of the BRB system was improved with innovative lateral-resisting systems of double-stage yield buckling-restrained brace (DYB), and a combination of DYB improved with shape memory alloy (SMA) materials...
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Enhancing seismic performance of rigid and semi-rigid connections equipped with SMA bolts incorporating nonlinear soil-structure interaction
PublicationNowadays, using smart connections can improve the performance of buildings with some recentering features that are from the superelastic behavior of Shape Memory Alloys (SMAs). It seems that there is different rigidity between the designed connection and the real one in Steel Moment-Resisting Frames (SMRFs), which can be considered as a problematic issue due to the importance of connections in seismic performance assessment. This...
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Enhancing Seismic Performance of Semi-rigid Connection Using Shape Memory Alloy Bolts Considering Nonlinear Soil–Structure Interaction
PublicationSteel Moment-Resisting Frames (SMRFs) have their lateral resistance for their rigid connections, while real conditions have shown that the rigidity of a connection depends on the bolts and the end-plate thickness, which may not provide the assumed rigidity in design process. In this research, the main goal is to enhance the semi-rigid connections using shape memory alloy (SMA) bolts and explore their effects on the seismic limit-state...
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Introducing a Computational Method to Retrofit Damaged Buildings under Seismic Mainshock-Aftershock Sequence
PublicationRetrofitting damaged buildings is a challenge for engineers, since commercial software does not have the ability to consider the local damages and deformed shape of a building resulting from the mainshock record of an earthquake before applying the aftershock record. In this research, a computational method for retrofitting of damaged buildings under seismic mainshock-aftershock sequences is proposed, and proposed computational...
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Machine learning-based prediction of preplaced aggregate concrete characteristics
PublicationPreplaced-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
PublicationPreplaced-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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Machine learning-based prediction of residual drift and seismic risk assessment of steel moment-resisting frames considering soil-structure interaction
PublicationNowadays, due to improvements in seismic codes and computational devices, retrofitting buildings is an important topic, in which, permanent deformation of buildings, known as Residual Interstory Drift Ratio (RIDR), plays a crucial role. To provide an accurate yet reliable prediction model, 32 improved Machine Learning (ML) algorithms were considered using the Python software to investigate the best method for estimating Maximum...
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Machine learning-based prediction of seismic limit-state capacity of steel moment-resisting frames considering soil-structure interaction
PublicationRegarding 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-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures
PublicationMany 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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Machine learning-based seismic response and performance assessment of reinforced concrete buildings
PublicationComplexity 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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Optimal retrofit strategy using viscous dampers between adjacent RC and SMRFs prone to earthquake‑induced pounding
PublicationNowadays, retrofitting-damaged buildings is an important challenge for engineers. Finding the optimal placement of Viscous Dampers (VDs) between adjacent structures prone to earthquake-induced pounding can help designers to implement VDs with optimizing the cost of construction and achieving higher performance levels for both structures. In this research, the optimal placement of linear and nonlinear VDs between the 3-story, 5-story,...
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Optimum number of actuators to minimize the cross-sectional area of prestressable cable and truss structures
PublicationThis paper describes a new computational method for determining the optimum number of actuators to design the optimal and economic cross-sectional area of pin-jointed assemblies based on the conventional force method. The most active members are selected to be prestressed to redistribute stress in the whole structure, resulting in regulating the internal force of bars that face high stress. Reducing stress in critical members allows...
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Predicting seismic response of SMRFs founded on different soil types using machine learning techniques
PublicationPredicting the Maximum Interstory Drift Ratio (M-IDR) of Steel Moment-Resisting Frames (SMRFs) is a useful tool for designers to approximately evaluate the vulnerability of SMRFs. This study aims to explore supervised Machine Learning (ML) algorithms to build a surrogate prediction model for SMRFs to reduce the need for complex modeling. For this purpose, twenty well-known ML algorithms implemented in Python software are trained...
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Probabilistic assessment of SMRFs with infill masonry walls incorporating nonlinear soil-structure interaction
PublicationInfill Masonry Walls (IMWs) are used in the perimeter of a building to separate the inner and outer space. IMWs may affect the lateral behavior of buildings, while they are different from those partition walls that separate two inner spaces. This study focused on the seismic vulnerability assessment of Steel Moment-Resisting Frames (SMRFs) assuming different placement of IMWs incorporating nonlinear Soil-Structure Interaction (SSI)....
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Seismic performance evaluation of steel buckling-restrained braced frames including SMA materials
PublicationThe permanent deformation of the building after seismic excitations can be determined by the Maximum Residual Interstory Drift Ratio (MR-IDR), which may be used for measuring the damage states. Low-post yield stiffness of the steel buckling-restrained braced frame (BRBF) makes this system vulnerable to large MR-IDR after a severe earthquake event. To overcome this issue, this paper investigates the seismic limit state performances...
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Seismic Response Analysis of Knee-Braced Steel Frames Using Ni-Ti Shape Memory Alloys (SMAs)
PublicationShape Memory Alloys (SMAs) are known as active materials that can be widely used for structural purposes due to their flag-shape behavior under loading and reloading. Their unique characteristics provided a potential solution for civil engi-neers especially to model buildings with the capability of dissipating seismic en-ergy. In this study, the main purpose is to explore the seismic behavior of Knee-Braced Frames (KBFs) and...
Year 2022
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Development of fragility curves in adjacent steel moment-resisting frames considering pounding effects through improved wavelet-based refined damage-sensitive feature
PublicationFragility curves present useful information related to earthquake-induced probability assessment of steel moment-resisting frames (MRFs) and determine the probability of the damage exceedance at different floor levels of MRFs. The review of the literature shows that most of the previous studies dealing with the fragility curves were based on conventional measures, such as spectral acceleration at the first mode period, peak...
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Investigating an Optimal Computational Strategy to Retrofit Buildings with Implementing Viscous Dampers
PublicationCivil engineering structures may seriously suffer from different damage states re-sult of earthquakes. Nowadays, retrofitting the existing buildings is a serious need among designers. Two important factors of required performance level and cost of retrofitting play a crucial role in the retrofitting approach. In this study, a new optimal computational strategy to retrofit structures by implementing linear Viscous Dampers (VDs)...
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Performance of Vector-valued Intensity Measures for Estimating Residual Drift of Steel MRFs with Viscous Dampers
PublicationViscous Dampers (VDs) are widely used as passive energy dissipation system for improving seismic performance levels especially in retrofitting of buildings. Residual Inter-story Drift Ratio (R-IDR) is another important factor that specifies the condition of building after earthquake. The values of R-IDR illustrates the possibility of retrofitting and repairing of a building. Therefore, this study aims to explore the vector-valued...
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Prediction of fracture toughness in fibre-reinforced concrete, mortar, and rocks using various Machine learning techniques
PublicationMachine 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...
Year 2021
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Predicting the seismic collapse capacity of adjacent SMRFs retrofitted with fluid viscous dampers in pounding condition
PublicationSevere damages of adjacent structures due to structural pounding during earthquakes have emphasized the need to use some seismic retrofit strategy to enhance the structural performance. The purpose of this paper is to study the influence of using linear and nonlinear Fluid Viscous Dampers (FVDs) on the seismic collapse capacities of adjacent structures prone to pounding and proposing modification factors to modify the median...
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Seismic damage diagnosis in adjacent steel and RC MRFs considering pounding effects through improved wavelet-based damage-sensitive feature
PublicationThis paper aims to propose complex Morlet (cmorfb-fc) wavelet-based refined damage-sensitive feature (rDSF) as a new and more precise damage indicator to diagnose seismic damages in adjacent steel and Reinforced Concrete (RC) Moment Resisting Frames (MRFs) assuming pounding conditions using acceleration responses. The considered structures include 6- and 9-story steel and 4- and 8-story RC benchmark MRFs that are assumed to have...
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Seismic damage diagnosis in adjacent steel and RC MRFs considering pounding effects through improved wavelet-based damage-sensitive feature
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Seismic damage diagnosis in adjacent steel and RC MRFs considering pounding effects through improved wavelet-based damage-sensitive feature
PublicationThis paper aims to propose complex Morlet (cmorfb-fc) wavelet-based refined damage-sensitive feature (rDSF) as a new and more precise damage indicator to diagnose seismic damages in adjacent steel and Reinforced Concrete (RC) Moment Resisting Frames (MRFs) assuming pounding conditions using acceleration responses. The considered structures include 6- and 9-story steel and 4- and 8-story RC benchmark...
Year 2020
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Predicting the seismic collapse capacity of adjacent structures prone to pounding
PublicationIn crowded cities, many structures are often constructed in a very close vicinity; therefore, during severe earthquakes, pounding phenomenon occurs due to out-of-phase vibrations of adjacent structures. In this study, pounding of adjacent structures is investigated up to the occurrence of total collapse. The novelty of this study is performing incremental dynamic analyses to compute the seismic collapse capacities of both pounding...
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Predicting the seismic collapse capacity of adjacent structures prone to pounding
Publication
Year 2018
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Evaluation the P-Delta Effect on Collapse Capacity of Adjacent Structures Subjected to Far-field Ground Motions
PublicationIn urban areas, adjacent structures can be seen in any insufficient distance from each other, because of economic reasons and refusal of acquired minimum separation distance according to seismic previsions. Collapse capacity assessment of structures is one of the important objectives of performance-based seismic engineering. The purpose of this study is to consider the pounding phenomenon and P-Delta effect in seismic collapse...
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Evaluation the P-Delta Effect on Collapse Capacity of Adjacent Structures Subjected to Far-field Ground Motions
Publication
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