Revolutionizing Timber Structures: A Comprehensive Review on AI-Driven Structural Design, Performance Optimization, and Future Perspectives
Comparison of L-Moments of Extreme Value Family of Probability Distributions for Determination of Design Rainfall Depth
The Study on Impact of Traffic and Pavement Strength on Cracking of Flexible Pavement with Age from Indian Highways Context by using HDM-4 and MEPDG Deterioration Equations
The Ideal Site Selection
Study on Compressive Strength of M40 Grade Concrete with Colloidal Silica as Replacement and Addition
Study on Strength Properties of Lightweight Expanded Clay Aggregate Concrete
A Step By Step Illustrative Procedure to Perform Isogeometric Analysis and Find the Nodal Displacements for a Two Dimensional Plate Structure
Lateral - Torsional Buckling of Various Steel Trusses
A Step by Step Procedure to Perform Isogeometric Analysis of Beam and Bar Problems in Civil Engineering Including Sizing Optimisation of a Beam
Comparative Study on Methodology of Neo-Deterministic Seismic Hazard Analysis Over DSHA and PSHA
Investigation on the Properties of Non Conventional Bricks
Analysis on Strength and Fly Ash Effect of Roller Compacted Concrete Pavement using M-Sand
Investigation on Pozzolanic Effect of Mineral Admixtures in Roller Compacted Concrete Pavement
Effect of Symmetrical Floor Plan Shapes with Re-Entrant Corners on Seismic Behavior of RC Buildings
Effect of Relative Stiffness of Beam and Column on the Shear Lag Phenomenon in Tubular Buildings
This comprehensive review examines the transformative impact of artificial intelligence (AI) on timber structural engineering, emphasizing advancements in AI-driven design, performance optimization, and future directions. Timber has experienced a renaissance as a sustainable construction material, bolstered by the development of engineered wood products such as Cross- Laminated Timber (CLT) and glued laminated timber (Glulam). These products offer enhanced structural performance, aesthetic versatility, and environmental benefits. However, traditional timber design methods, which largely rely on empirical approaches, are increasingly inadequate for addressing modern performance demands and complex architectural geometries. AI techniques, including machine learning, deep learning, and genetic algorithms, are revolutionizing timber engineering by enabling precise material characterization, load path optimization, and real-time failure prediction. By integrating these methods with conventional finite element analysis (FEA), researchers have significantly reduced computation times while improving simulation accuracy. Furthermore, the incorporation of sensor-based data acquisition and Building Information Modeling (BIM) facilitates dynamic monitoring and predictive maintenance, ensuring long-term structural integrity. This review synthesizes literature on AI applications in timber design, detailing the evolution from traditional methods to current digital practices. It discusses the benefits and challenges of integrating AI into simulation workflows, performance monitoring, and digital fabrication processes. Additionally, the paper outlines future research opportunities, including multi-scale modeling, sustainability lifecycle assessments, and the development of adaptive, integrated design systems that fully leverage AI capabilities. Overall, our analysis highlights the potential of AI-driven frameworks to enhance the safety, efficiency, and sustainability of timber structures, providing a roadmap for future innovations in the field. By bridging the gap between traditional construction practices and advanced computational technologies, this review offers valuable insights for researchers, engineers, and practitioners dedicated to advancing modern timber engineering.
Accurate estimation of extreme (i.e., 1-day maximum) rainfall is essential for water resources management, flood forecasting, agricultural planning, and climate change impact studies. This can be achieved through fitting the extreme value family of probability distributions (EVD) that consists of Extreme Value Type-1 (EV1), Extreme Value Type-2 (EV2), Generalized Extreme Value (GEV), and Generalized Pareto (GPA) to the series of observed annual 1-day maximum rainfall (AMR), whereas the parameters are determined by the Method of L-Moments (LMO). This paper presents a study on the comparison of LMO estimators of EVD for the determination of design rainfall depth at Akkalkuwa, Kamrej, Navapur, Sakri, Shahada, and Taloda rain gauge sites. For this purpose, the AMR series was generated from the daily rainfall data observed at the sites during the period 1960 to 2022 and used. The adequacy of fitting LMO of EVD to the AMR series was examined through Goodness-of-Fit (GoF) tests, viz., Chi-Square and Kolmogorov-Smirnov (KS), while the selection of the best-fit distribution was made through model performance analysis with various indicators, viz., correlation coefficient (CC), Nash- Sutcliffe model efficiency (NSE), root mean squared error (RMSE), and cross-correlation matrix analysis (CCMA). The Chi- Square test results uniformly supported the use of EV1 and GEV for modelling the AMR data of six sites, whereas KS test results supported all four distributions of EVD for all six sites. The results indicated that the CC values obtained from four distributions vary between 0.960 and 0.994. The study showed that the NSE computed by EV1, GEV, and GPA varies from 91.7% to 98.7%. The outcomes of CCMA showed that there is a perfect correlation between the estimated rainfall by EV1 and GEV, and also nearer to 1.000. On the basis of evaluation of the results with quantitative (viz., CC, NSE, and RMSE) and qualitative assessments, it was found that GEV is the best choice for rainfall estimation for Akkalkuwa, Kamrej, Navapur, Sakri, Shahada, and Taloda. The estimated extreme rainfall by GEV distribution could be considered as a design rainfall depth while planning water resources management projects and their related activities in the respective sites.
Pavement maintenance is the major activity for keeping any highway in good condition, especially for flexible pavement, and for planning future maintenance requirements, prediction of pavement distresses plays a major role. In India, cracking in flexible pavement is one of the major distresses, which is mainly caused by low pavement strength and heavy traffic loading, apart from other factors such as age, materials, environment, and construction quality. This paper is the theoretical study and analysis of the impact of traffic and pavement strength on cracking of flexible pavement from an Indian highways perspective. The deterioration equations of flexible pavement used in this study for analysis are deterioration models of HDM-4, Indian models, and MEPDG. The first two models are empirical, based on historical pavement distress data, whereas the third model is a mechanistic-empirical model based on material properties, stress/strain in pavement layers, and historical pavement distress data.
The rapid growth of cities has led to a significant shortage of affordable housing, exacerbated by unplanned development and rising population density. This work was initiated to tackle the pressing housing issues by using datadriven analysis to identify neighborhoods that offer a balanced mix of essential amenities. The main goal is to support homebuyers by recommending neighborhoods that are well-equipped with necessary amenities, thus enabling more informed and efficient housing choices in complex urban areas. This methodology can be applied to other cities facing similar challenges related to urban expansion. For this work, a Foursquare Places API is used to gather data on various amenities in different neighborhoods, including hospitals, supermarkets, parks, and public transportation services. Additionally, a housing price data was incorporated to provide budget options, allowing potential buyers to find neighborhoods within their financial limits. Through K-Means clustering, we grouped neighborhoods based on the availability of amenities, aiming to highlight areas with the most potential for growth and affordability. The clustering results were evaluated based on the number of clusters and the distribution of neighborhoods within them. K- Means proved effective in grouping neighborhoods with similar amenities, helping us identify areas that offer a balanced mix of amenities and affordability. The algorithm provided valuable insights by organizing neighborhoods into distinct clusters, giving homebuyers a clearer understanding of the best options available within their budget.
Nowadays the use of concrete is increased due to the rapid growth of the construction industry, especially in the urban areas. Concrete, being an essential building material, has gained significant attention in the construction industry worldwide. Colloidal silica is a potential material to improve various concrete properties such as workability, strength, and durability. The percentage addition of colloidal silica in concrete has become an important topic of research these days due to its beneficial effects on concrete strength enhancement. This study aims to evaluate the effect of colloidal silica addition and replacement with cement by weight on the compressive strength of M40 grade concrete. The strength of concrete is affected by various factors such as the water-cement ratio, the type of aggregate, cement, and the admixture. The effect of colloidal silica on the workability, density, and compressive strength of M40 grade concrete was studied in the present study using colloidal silica of type CS-TX. Colloidal silica is a nanomaterial that has been widely used in recent years. Colloidal silica of the CS–TX type is replaced and added with cement at various percentages like 1.0, 1.5, 2.0, 2.5, and 3.0%, respectively, to study the improvement in compressive strength of M40 grade concrete. Use of colloidal silica increases the compressive strength by reducing the voids in the concrete. Experimental results indicate that 2% of colloidal silica is optimum for replacement of cement and 2.5% of colloidal silica is the optimum content for addition of cement in M40 grade concrete.
