2025· Proceedings of the 1st International Conference on Interdisciplinary Technology & Science Convergence (FusionX Global)· pp. 672-678· 0 citations· 16 references
TL;DR
Among the various components of reinforced concrete structures, slabs form a fundamental load-bearing element that governs both structural performance and overall building economy.
Abstract
: In recent years, the integration of Artificial Intelligence (AI) into civil engineering has become increasingly significant as the profession moves towards automation, precision, and data-driven decision-making. Conventional design and analysis methods, though reliable, are often time-consuming and depend heavily on manual interpretation of standards and calculations. AI-driven tools are now being explored to assist engineers in performing complex tasks such as structural design validation, quantity estimation, and optimization of material use. These intelligent systems have shown potential to minimize human error, enhance design accuracy, and accelerate the decision-making process in construction projects. Among the various components of reinforced concrete structures, slabs form a fundamental load-bearing element that governs both structural performance and overall building economy. Reinforced Cement Concrete (RCC) slabs, whether one-way, two-way, or flat, are designed to resist bending and shear.
The construction industry is increasingly adopting offsite panelised systems, particularly light gauge steel (LGS) panel solutions, as a means of improving productivity, reducing waste, and accelerating project delivery. However, a critical challenge remains in accurately determining the quantity of LGS panels and any supporting hot-rolled steel (HRS) requirements for a given building project in the early stages of tendering, design, and planning. Estimating LGS and HRS quantities involves complex interdependencies between architectural configurations, structural requirements, building typologies, and site-specific constraints. Traditional estimation methods, which rely heavily on expert judgement and manual quantification processes, are time-consuming, inconsistent, and prone to error, often resulting in procurement inefficiencies. Despite growing research on artificial intelligence (AI) and machine learning (ML) applications in construction, their specific application to offsite LGS panel quantity estimation remains largely unexplored. Data-driven approaches capable of processing multiple building parameters simultaneously are needed to generate reliable quantity predictions for early-stage decision-making. The presented research proposes a conceptual framework for applying AI and ML techniques to estimate LGS panel and HRS requirements for building projects. The framework identifies key input parameters including building geometry, floor area, storey height, structural system type, complexity, and opening ratios. A structured methodology is outlined encompassing data requirements, feature identification, model selection considerations, and validation approaches. The framework addresses implementation challenges including data scarcity in offsite construction, workflow integration, and interpretability of model outputs for industry practitioners. The proposed framework has the potential to improve the accuracy and efficiency of LGS and HRS quantity estimation, support better procurement planning, and reduce material wastage. This conceptual contribution, developed through a University College Dublin – Evolusion Innovation partnership, provides a structured foundation for future empirical studies and AI tool development in offsite construction.
B. Gülmez, M. M. Osman, D. Wallace et al.· 2026: Transforming Construct...· 0 citations
When civil engineering structures are exposed to unpredictable loading circumstances, structural reliability evaluation is crucial for assuring their safety and performance. When testing how structures react to different random variables, the traditional numerical methods that rely on running finite element simulations again and again are computationally intensive. A framework for dependability analysis of multistorey frame structures exposed to variable lateral and combined loading conditions is proposed in this research, which is based on a mix of artificial intelligence techniques. Using SAP2000, numerical datasets were generated for a two-span, six-story plane frame structure that takes into account lateral stresses, gravity loads, and fluctuations in modulus of elasticity as unknown factors. By combining the results of the structure response data generation process with the optimization algorithms of the Random Forest (RF), Whale (WOA), and Sparrow Search (SSA), a hybrid machine learning model was created. In order to forecast the crucial node's lateral displacement under various loading situations, the models that were constructed were trained and evaluated. Utilizing statistical indicators such as NashSutcliffe efficiency (NS), adjusted R², performance index (PI), variance account factor (VAF), Legate and McCabe index (LMI), Willmott index (WI), root mean square error (RMSE), mean absolute error (MAE), weighted mean absolute percentage error (WMAPE), and scatter index (SI), the suggested models' performance was assessed. In addition, the models' reliability performance was evaluated using the FOSM approach, which measures the reliability index (Η) and the probability of failure (Pf). According to the findings, each of the suggested hybrid models managed to get a remarkable level of prediction accuracy. The RF-WOA model showed better reliability performance and generalizability for structures that were loaded floor-wise laterally. During the testing and training stages, RF-DOA obtained the best prediction accuracy when subjected to a combination of gravity and lateral loads. The results of the reliability study show that hybrid AI models are a good substitute for computationally heavy structural evaluation methods and can accurately forecast the reaction of structures.
ALAGONDA NANDINI, Mrs. M. Swathi, Dr. B. SHARATH CHANDRA· International Journal of Eng...· 0 citations
The implementation of the ground deceleration function in civil aircraft represents a critically complex process that deeply relies on the seamless collaboration of multiple onboard systems, including but not limited to braking, thrust reversal, spoiler, and steering systems. The operational logic governing these systems is highly intricate, characterized by tightly coupled interactions, stringent safety requirements, and a vast array of diverse physical and logical interfaces. This inherent complexity makes it exceptionally difficult to gain a thorough, system-level understanding of the implementation mechanisms and collaborative principles solely through traditional means of examining extensive, yet often fragmented, design documentation. The limitations of document-based analysis frequently lead to unforeseen integration conflicts, which are typically discovered late in the development cycle, resulting in substantial rework costs and project delays. To address this pervasive industry challenge, this paper selects the aircraft ground deceleration function as a representative case study and proposes an innovative, simulation-based validation methodology. This approach systematically utilizes model state machines to create a dynamic digital representation of the system-of-systems, enabling rigorous validation of aircraft deceleration requirements under various operational scenarios. By adopting this model-based systems engineering (MBSE) paradigm for mechanism representation, our approach effectively captures the nuanced coordination, timing dependencies, and dynamic interactions within the multi-system operational logic. It thereby facilitates the intuitive identification, analysis, and resolution of potential design flaws, including logical conflicts, deadlocks, race conditions, and uncovered or ambiguous requirements. Consequently, the method not only provides a robust framework for validating the aircraft’s function-related design requirements with greater confidence but also offers crucial, data-driven support for the iterative optimization and evolution of the overall functional architecture. The fundamental value proposition of this research lies in its transformative capability to convert implicit design knowledge and assumptions—originally scattered across voluminous documents, specifications, and expert minds—into an integrated set of executable, observable, and analyzable formal models. This digital thread enables systems engineers and designers to identify deep-seated integration and coordination issues proactively during the early conceptual and detailed design stages, rather than relying on discovery during the late, costly integration and testing phases. By shifting validation left in the development V-cycle, this approach significantly reduces the risk of major design changes and associated cost overruns later in the project lifecycle. Ultimately, it effectively enhances the overall maturity, safety, certifiability, and operational reliability of complex aircraft function development, paving the way for more efficient and predictable engineering processes.
Mingqian Wang, Q. Yu, Miao Yu et al.· SAE technical paper series· 0 citations
S-lay installation remains the dominant method for offshore pipeline deployment, with inline components — valves,
flanges, Inline Tee (ILT) assemblies, and Pipeline End Terminations (PLETs) — increasingly integrated into the continuous
pipeline string. As these structures traverse the stinger, the displacement-controlled overbend condition induces strains in the
adjacent pipeline that exceed those in plain pipe, a complex and highly localized design condition unique to such subsea
assemblies. For a given piping layout, many structural configurations are possible, and identifying the one that minimizes
installation strain is a non-trivial search problem that fast-track project schedules rarely allow engineers to conduct
systematically; designs are instead adapted from past projects using individual pattern recognition rather than a structured body
of design knowledge. This paper is the first in a series developing an AI-assisted conceptual engineering framework for subsea
inline structures, using the S-Lay ILT design problem as its case study. It introduces a dual classification system — a physical
taxonomy (IW/EA) describing how components connect to the pipeline, and a mechanical taxonomy (Types A, B, C) describing
their strain-amplification behaviour — and characterizes each behaviour type through parametric finite element analyses
incorporating stinger–pipeline contact modelling. Results identify offset depth and component length as the dominant strain
drivers for elevation- and stiffness-type components respectively, quantify the DNV-compliant range for taper transitions, and
show that very long components can paradoxically reduce peak strain through dual-roller contact. Together, these findings
constitute the design intuition — Steps 1 and 2 of the proposed seven-step framework — needed to support AI-assisted
conceptual design of subsea structural assemblies
Sreekanth Manakkattil Sivaraman, Jagannatha Venakataramana Reddy· International Journal for Re...· 0 citations
Artificial Intelligence (AI)–enhanced Computer-Aided Manufacturing (CAM) is transforming advanced manufacturing, including aerospace and energy sectors, by enabling precise, efficient, and reliable production of complex components. Through a synthesis of recent literature and theoretical analysis, this study highlights how AI-driven CAM optimizes toolpaths, improves predictive maintenance, and enhances machining accuracy and structural integrity. Key advances from 2020–2025, such as digital twins, adaptive toolpath strategies, hybrid additive-subtractive processes, and smart machining environments, demonstrate reduced errors, faster production cycles, and improved integration between design and manufacturing teams. Challenges remain in data availability, implementation costs, workforce readiness, cybersecurity, and process integration. The paper contributes by providing updated insights into AI-CAM synergy and strategies for practical industrial adoption.
T. Ogedengbe, S. Ogunlowo, O. V. Eşidir et al.· SPE Nigeria Annual Internati...· 0 citations
Steel beams are efficient structural elements widely used in industrial buildings due to their remarkable strength and ductility characteristics. Despite numerous studies exploring the optimization of steel beam for cost-effective designs, the utilization of optimization within the construction industry remains rare. This scarcity can be attributed to the complexities associated with applying optimization algorithms and the limited understanding of the structural behavior of optimized designs. In light of these challenges, this study aims to leverage the immense computational power of artificial intelligence (AI), specifically the Evolutionary Algorithm (EA), to develop an innovative AI-driven spreadsheet-based tool for cost optimization of I-shaped steel beams. Additionally, a parametric study investigates the influence of various design variables on the optimized cost. The EA within the Solver tool of MS Excel is used to perform the optimization. Design variables are subject to strength and serviceability-related constraints in accordance with AISC 360–22. The effectiveness of the developed optimization approach is demonstrated by optimizing four steel beam design examples from the literature. It is found that up to 51% of the beam cost can be optimized by keeping the beam depth and steel grade as variables. The parametric study reveals that the optimal range for the beam depth varies depending on the steel grade used. Further, the trends obtained for beam cost with respect to other variables such as flange and web slenderness ratio, and beam depth provide valuable insights for structural engineers undertaking steel beam optimization in the future.
Rizwan Azam, M. Riaz, Rana Muhammad Junaid et al.· Discover Civil Engineering· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
