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![Distribution of period [Sec.] by various building characteristics](publication/380793015/figure/fig3/AS:11431281262640133@1721701177496/Distribution-of-period-Sec-by-various-building-characteristics_Q320.jpg)
This research explores the use of Deep Symbolic Regression (DSR) to develop a sophisticated predictive model for the fundamental period of vibration in concentrically steel-braced reinforced concrete (RC) frames. Traditional empirical models often overlook complex interactions within structural dynamics during seismic events, a gap this study addre...
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The seismic risk associated with old and existing buildings in Türkiye has surged in significance, emphasizing the need for a detailed exploration of their behavior, particularly in the face of earthquake hazards. Given the widespread use of smooth bars in older reinforced elements, understanding the nonlinear response of these elements is imperati...
Citations
... In recent years, machine learning models have been increasingly applied to predict structural performance and optimize maintenance strategies, offering a data-driven approach that enhances decision-making in civil infrastructure projects [8,9]. The potential of machine learning techniques, such as symbolic regression, has been demonstrated in formulating critical parameters in structural systems, which can be adapted for assessing pavement conditions and determining optimal maintenance interventions [10]. ...
The rapid urban development in Uttara, Dhaka, has significantly strained its road infrastructure, with 15% of the paved roads suffering from various failures. This study investigates the types of flexible pavement failures in the Uttara region and explores the underlying causes. Factors such as low-quality materials, heavy truck loads, intense rainfall, and poor drainage systems contribute to the degradation of road surfaces. Pavement failures observed include cracking, potholes, rutting, and depressions, causing disruptions in traffic flow and increased vehicle maintenance costs. A comprehensive field survey was conducted across two major routes, and the pavement condition was evaluated using visual inspections and quantitative measurements. The results show that Route 1 requires overlay maintenance, while Route 2 requires routine maintenance. The study provides targeted recommendations for improving road durability, such as enhanced drainage systems, regular maintenance schedules, and better traffic load management. These findings are intended to assist municipal authorities in developing more effective maintenance strategies for flexible pavements in urban areas with tropical climates.
... ML techniques such as neural networks, support vector machines, and decision trees have been utilized for structural system identification, health monitoring, and vibration control [2][3][4]. ML models can extract patterns from data, making them invaluable for assessing structural performance and informing preemptive and recovery decisions [5,6]. ...
Infill wall systems have been extensively studied as potential solutions to mitigate the adverse effects of stiffness irregularity in soft story structures. This research leverages unsupervised machine learning techniques, specifically clustering algorithms, to analyze and compare the mechanical behavior of different columns in a nine-story unsymmetrical reinforced concrete building subjected to seismic loading. The primary objective is to assess the effectiveness of various infill wall systems, including 5-inch and 10-inch wide brick walls and 5-inch, 7.5-inch, and 10-inch wide concrete walls, in enhancing structural resilience. The study employs a comprehensive data-driven approach, incorporating ETABS modeling, data preprocessing, exploratory data analysis, and clustering to identify patterns and relationships in the structural performance of columns. Key findings indicate that wider and concrete infill walls significantly reduce displacement values, thereby improving structural stiffness. Cluster analysis reveals that columns connected to multiple infill walls, particularly exterior corner columns, exhibit enhanced structural performance. Specifically, the 10-inch wide concrete infill wall system demonstrated the highest efficacy in mitigating stiffness irregularity. The research further highlights that clustering algorithms, such as K-Means, are effective in categorizing columns based on their mechanical responses, facilitating a comparative evaluation of infill wall systems. These insights provide valuable recommendations for the design and retrofitting of soft story structures to enhance seismic resilience.
Understanding the seismic performance of Reinforced Concrete (RC) buildings is crucial for ensuring structural safety in earthquake-prone regions. This study examines the impact of different types of steel Concentrically Braced Frames (CBF) on the fundamental periods of RC buildings, following the BNBC 2020 guidelines. Utilizing ETABS 2021 for computational modeling, the research comprehensively analyzes RC buildings' dynamic behavior with diagonal (D-bracing), cross (X-bracing), and inverted V-bracing systems. Key structural parameters were evaluated to understand their influence on the fundamental periods, including total height, building length, building width, bracing moments of inertia, and total length of bracing. The study revealed that the BNBC 2020 guidelines, which use a general formula for predicting fundamental periods, do not accurately capture the dynamics of buildings with specific bracing configurations, with R² values of 0.65576 for D-bracing, 0.62273 for X-bracing and 0.64396 for inverted V-bracing. To address this limitation, new predictive equations were developed using linear regression in OriginLab, achieving substantially higher R² values of 0.96433 for D-bracing, 0.94696 for X-bracing, and 0.95757 for inverted V-bracing. These results demonstrate the superior accuracy of the proposed equations. The findings underscore the critical role of bracing types in enhancing the seismic performance of RC buildings. By providing tailored predictive models, this study offers valuable tools for engineers and designers, contributing to more accurate and reliable seismic design practices. The proposed equations enable the optimization of RC building designs for improved safety and resilience in seismic regions, thereby advancing the field of structural engineering.
