Muayad Habashneh

The BESO method is widely used to find efficient structural layouts through iterative material removal and addition. The method works well under linear conditions but things get more complicated when geometric nonlinearity is involved. Large displacements affect how the structure reaches equilibrium and this directly changes the sensitivity field and material update process making parameter selection much more critical. This paper studies three parameters systematically: mesh size, evolutionary rate and filter radius. Three benchmark structures are tested including an L-shaped beam, a pinned rectangular plate and a U-shaped plate. Each parameter is changed one at a time while the others stay fixed. Nodal displacement at the load point is used alongside topology results as a direct stiffness measure. Results show that the main load path stays intact across all tested cases but secondary bracing members are noticeably affected by coarser meshes, higher evolutionary rates and larger filter radii. The filter radius had the strongest overall influence on both topology and stiffness. The findings provide practical guidance for parameter selection and highlight the need to critically reassess algorithmic settings developed for linear BESO before applying them in large-displacement structural optimization.

The growing demand for concrete in tropical regions faces two unresolved challenges: the high carbon footprint of ordinary Portland cement (OPC) and limited understanding of how supplementary cementitious materials affect the mechanical performance of laterite rock aggregates concrete. Although metakaolin (MK) is a highly reactive pozzolan, its combined use with laterite rock aggregates concrete and its influence on strength development and microstructure have not been sufficiently clarified. This study investigates the mechanical behavior and sustainability potential of laterite rock aggregate concrete in which OPC is partially replaced by MK at 0%, 5%, 10%, 15%, and 20% by weight. All mixes were prepared at a constant water–binder ratio of 0.50 and tested for workability, compressive strength, split-tensile strength, and flexural strength at 7, 14, and 28 days, with and without a polycarboxylate-based superplasticizer. The results show that MK significantly enhances the mechanical performance of laterite rock concrete, with an optimum at 10% replacement: the 28-day compressive strength increased from 35.6 MPa (control) to 53.9 MPa in the superplasticized mix, accompanied by corresponding gains in tensile and flexural strengths. SEM–EDS analyses revealed microstructural densification, reduced portlandite, and a refined interfacial transition zone, explaining the improved strength and cracking resistance. From an environmental perspective, a 10% MK replacement corresponds to an approximate 10% reduction in clinker-related CO₂ emissions, while the use of locally available laterite rock reduces the dependence on quarried granite and transportation impacts. The findings demonstrate that MK-modified laterite rock concrete is a viable and eco-efficient option for structural applications in tropical regions. The study concludes that MK-enhanced laterite rock aggregate concrete can deliver higher structural performance and improved sustainability without altering conventional mix design and curing practices.

A geometrically nonlinear topology optimization method is presented in this paper for steel I-section beams, incorporating large displacement analysis to capture realistic structural behavior under flexural loads. The developed framework aims to enhance structural performance and material efficiency by optimizing the web region while preserving critical load paths. The optimization process is driven by enhancing the Bi-directional Evolutionary Structural Optimization (BESO) algorithm implemented in MATLAB and coupled with ABAQUS. Three beam configurations are analyzed under identical boundary and loading conditions to compare the performance of the optimized layout against traditional designs. To further assess the robustness of the proposed algorithm, two additional load scenarios, including a four-point bending test and a uniformly distributed load, are investigated. The results demonstrate that the optimized beam configurations reduce web material by 60% (volume fraction = 0.40) under 45 kN mid-span load while sustaining 55% higher load than the circular-opening beam (45 kN vs. 29 kN) with mean stress of 203.1 MPa, achieving 20% less web material than the conventional design and 18% higher complementary work (1.89 × 10 ⁵ N mm) than the plain-web beam. The findings underline the value of incorporating geometric nonlinearity into topology optimization for producing high-performance, lightweight steel structures suitable for real-world engineering applications.

This work presents an innovative framework for thermoelastic-plastic reliability-based topology optimization, tackling challenges related to material uncertainties, geometric imperfections, and variations in volume fractions. An enhanced Bi-directional Evolutionary Structural Optimization (BESO) method is developed. It integrates thermoelastic-plastic finite element analysis with stochastic reliability constraints to achieve robust and efficient structural designs under combined thermal and mechanical loading. The framework incorporates advanced modeling techniques, including temperature-dependent material properties, elasto-plastic behavior, and eigenmode-based imperfection modeling. A key innovation lies in formulating reliability constraints by treating volume fraction as a random variable to model material usage uncertainty. This ensures compliance with target safety indices. The proposed methodology is verified through detailed numerical examples, including steel beam and shell structures subjected to temperatures up to 800 °C. Results show that the probabilistic designs achieved up to 30% higher load-bearing capacity compared to deterministic ones and demonstrated improved stress distribution and thermal resilience. These enhancements confirm the method’s effectiveness in achieving optimal layouts that balance material efficiency, structural stability, and reliability.

In this manuscript, a novel approach to topology optimization is proposed which integrates considerations of uncertain load positions, thereby enhancing the reliability-based design within the context of structural engineering. Extending the conventional framework to encompass imperfect geometrically nonlinear analyses, this research discovers the intricate interplay between nonlinearity and uncertainty, shedding light on their combined effects on probabilistic analysis. A key innovation lies in treating load position as a stochastic variable, augmenting the existing parameters, such as volume fraction, material properties, and geometric imperfections, to capture the full spectrum of variability inherent in real-world conditions. To address these uncertainties, normal distributions are adopted for all relevant parameters, leveraging their computational efficacy, simplicity, and ease of implementation, which are particularly crucial in the context of complex optimization algorithms and extensive analyses. The proposed methodology undergoes rigorous validation against benchmark problems, ensuring its efficacy and reliability. Through a series of structural examples, including U-shaped plates, 3D L-shaped beams, and steel I-beams, the implications of considering imperfect geometrically nonlinear analyses within the framework of reliability-based topology optimization are explored, with a specific focus on the probabilistic aspect of load position uncertainty. The findings highlight the significant influence of probabilistic design methodologies on topology optimization, with the defined constraints serving as crucial conditions that govern the optimal topologies and their corresponding stress distributions.

This study investigates the impact of uncertain load positions through an innovative optimization technique. It seamlessly integrates reliability-based design into the optimization of the structural topology, with a specific focus on the web portion of steel I-beams and including geometrical imperfection analysis. The study operates under the premise that the applied load is randomly positioned, engaging in a comprehensive probabilistic analysis. This methodology extends its considerations to additional factors, such as material properties, volume fraction, and geometric imperfections. The assumption of a normal distribution for each of these parameters aids in quantifying uncertainties. Moreover, the proposed work leverages the notion of plastic ultimate load multipliers to illustrate how the algorithm can improve the performance of steel beams. To evaluate the algorithm, the results of a benchmark problem were meticulously analyzed. While exploring the probabilistic nature of externally applied force positions, a numerical example was conducted involving a steel I-beam within the context of reliability-based imperfect geometry topology optimization. The outcomes of the proposed method underscore that the integration of probabilistic design significantly influences the topology optimization process.

Existing studies often rely on deterministic numerical analyses for structural models. However, test results consistently highlight uncertainties, particularly in variables such as magnitude of the applied load, geometrical dimensions, material randomness, and limited experiential data. As a response, researchers have increasingly turned their attention to probabilistic design models, recognizing their crucial role in accurately predicting structural performance. This study aims to integrate reliability-based analysis into the numerical modeling of sinusoidal-web steel beams. Two sinusoidal-web beams are considered. The web and the flange thicknesses, in addition to the magnitude of the applied load, are treated as random variables with mean values and standard deviations. Notably, the study demonstrates the efficiency of the reliability index as a governing limit in the analysis process. A detailed comparison between deterministic and probabilistic designs of sinusoidal-web beams is conducted, focusing on the impact of introducing the nature of randomness. Therefore, this study’s results deepen our understanding of how uncertainties significantly influence deformations and stresses.

This mini review aims to summarize relevant international publications. Thus, based on this, giving a comprehensive review about the reinforcement solutions of permanent ways' soil substructure. Generally, the core weakness of soil is its inadequacy to resist tensile stresses. The main target of strengthening the soil is to enhance the engineering characteristics of the soil to build up specific parameters such as shear strength, compressibility, density, and hydraulic conductivity. In addition, special reinforcement techniques of railway permanent ways' soil substructures will be considered in this paper due to the increasing demand of improving railways and rehabilitation process. The main findings of this study that there are a lot of special reinforcement techniques which can be considered as effective solution for soil stabilization such as geosynthetic reinforcement.