Said Abdel-Monsef

Steel–concrete composite beams are widely used in bridge infrastructure but are vulnerable to deterioration due to uniform and pitting corrosion, particularly at the lower flange. This study investigates the flexural behavior of corroded steel–normal strength concrete (NSC) composite beams and evaluates rehabilitation using ultra-high-performance concrete (UHPC) slab replacement, with and without additional steel plate strengthening. A comprehensive finite element analysis was conducted considering three beam spans (5, 7, and 9 m), two corrosion types, and three corrosion levels. The results indicate that both corrosion types significantly reduce flexural capacity due to cross-sectional loss, with pitting corrosion causing greater strength reduction than uniform corrosion at the same weight loss because of stress concentration effects. Replacing the NSC slab with a UHPC slab effectively restores and often enhances load-carrying capacity beyond that of intact beams while reducing dead load, demonstrating the superiority of the proposed rehabilitation approach. The combined use of UHPC slab replacement and welded steel plate strengthening provides the greatest improvement, revealing a strong synergistic effect. A case study of a corroded steel bridge in Pennsylvania confirms the practical applicability of the method, showing that UHPC-based rehabilitation increases the load rating from below unity to above unity. These findings highlight UHPC as an efficient and sustainable solution for extending the service life of aging steel bridges.

Externally bonded carbon-fiber-reinforced polymer (CFRP) can increase the flexural capacity of steel beams, but the benefit is often limited by the performance of the adhesive interface. This study develops and validates a three-dimensional finite-element model (FEM) with an explicit cohesive-zone representation of the adhesive layer. It reproduced benchmark four-point bending tests in terms of peak load, corresponding mid-span deflection, and the transition from end/intermediate debonding to laminate rupture. A one-factor-at-a-time parametric analysis is carried out to examine the influence of (i) member geometry (beam depth; flange and web thickness), (ii) CFRP configuration (bonded length; laminate thickness), and (iii) bond quality (cohesive normal strength). Within the ranges studied, cohesive strength and bonded length are the primary variables controlling both capacity and failure mode: strengths below about 25 MPa and short plates lead to debonding-governed response. Increasing strength to around 27 MPa and bonded length to 650–700 mm delays debonding, promotes CFRP rupture, and produces the largest incremental gains in peak load, while further increases in length give smaller additional gains. Increasing laminate thickness and steel depth or flange/web thickness always raises peak load, but under baseline bond conditions failure remains debonding and the added material is only partially mobilized. When cohesive strength is increased above the threshold, additional CFRP thickness becomes more effective. A linear regression model is fitted to the FEM dataset to express peak load as a function of bonded length, cohesive strength, laminate thickness, and steel dimensions, and is complemented by a failure-mode map and a cost–capacity chart based on material quantities. Together, these results provide quantitative trends and simple relations that can support preliminary design of CFRP-strengthened steel beams for similar configurations.

The growing adoption of laminated fiber-reinforced polymer (FRP) composites in aerospace, automotive, and civil engineering demands advanced design methodologies capable of navigating their complex anisotropic behavior. While traditional design approaches rely heavily on iterative simulations and classical optimization, recent advances in artificial intelligence (AI) offer a transformative alternative. This review systematically examines the expanding role of AI in composite design and optimization—highlighting a critical transition from physics-based modeling to data-driven, intelligent frameworks. This paper emphasizes emerging AI paradigms not yet widely covered in the composite literature, including Explainable AI (XAI) for interpretable decision-making and Large Language Models (LLMs) for automating design synthesis and knowledge retrieval. Key findings demonstrate AI’s capacity to efficiently optimize stacking sequences, ply orientations, and manufacturing parameters while satisfying multi-objective constraints such as weight, stiffness, and damage tolerance. Furthermore, we explore AI’s integration across the composite lifecycle—from surrogate-assisted finite element analysis and uncertainty-aware design allowables to in-service structural health monitoring. By bridging the gap between computational intelligence and industrial practicability, this review underscores AI’s potential not as a supplementary tool, but as a foundational technology poised to redefine next-generation composite engineering.

In frame structures, connections play a vital role in governing both serviceability and ultimate strength. For pultruded fiber-reinforced polymer (PFRP) frames, connection design is even more critical due to the anisotropic and viscoelastic nature of the composite materials used in the primary elements (e.g., beams and columns) and their joints. This study presents a finite element model (FEM) to evaluate the influence of several connection parameters—namely, connection stiffening, bolt diameter, washer diameter, and clamping force—on the elastic behavior of beam-column joints composed of PFRP elements. The results demonstrate that stiffening the upper and lower connection angles significantly enhances joint performance. Increasing the bolt diameter improves moment capacity, reduces rotational deformation, decreases stress concentrations around bolt-hole edges, and increases both minor principal and compressive stresses beneath the bolt shank. Similarly, a larger washer diameter contributes to higher connection stiffness and reduces stress concentrations at bolt holes. Although the clamping force has a relatively modest effect on global connection behavior, it positively influences the through-thickness stress distribution in the angle beneath the bolt shank. Finally, regression equations were developed to quantify the relationship between rotation, moment, bolt diameter, washer diameter, and clamping force, providing a valuable tool for the design and optimization of PFRP connections in structural applications.

Aeronautical industries are concerned about generating efficient and cost-effective statistical design allowables of composite laminate responses. Within this framework of uncertainty quantification and management (UQ & M), accounting for the various uncertainties is crucial to calculate the design allowables which, in turn, adds confidence to the design and certification process. In this context, as a precursor to UQ & M, we establish a global sensitivity analysis framework to identify the material properties that influence the laminate responses. In this study, an open hole tensile (OHT) test configuration is considered. In a first step, sensitivity analysis based on an analytical model is performed using Sobol, FAST, and Morris methods, whereas in a second step, sensitivity analysis based on a detailed 3D finite element model (FEM) incorporating damage is performed using Morris. The paper compares different sensitivity approaches in terms of computational cost and results. Further, different case studies (varied notched hole sizes, width-to-diameter ratios, laminate stacking sequences, etc.) are performed to study how the sensitive parameters vary for different configurations. The study identified the Morris method, along with the selected hyper parameters, as being suitable for FEM based sensitivity analysis due to its low computational cost. Five material properties (out of 23) were identified as influencing the open hole laminate tensile strength. Using the FE simulations performed for the Morris analysis, a first estimation of the design allowables (A and B basis values) on the OHT strength is also presented. The results from this paper contribute to the next step of UQ&M, where only the identified sensitive material properties can be considered for detailed experimental characterization or for building meta models to calculate the design allowables. A framework like this, complements experimental campaign to reduce the associated costs and knock-down factors, thus shortening the certification process.