Machine Learning Outsmarts Design Codes in Predicting Shear Strength of Recycled Concrete Beams

Concrete is the most consumed man-made material on Earth, and the environmental bill for making it is staggering. Every year, billions of tonnes of natural gravel and crushed rock are quarried to feed the construction industry, while mountains of demolition waste pile up in landfills. One increasingly popular remedy is recycled concrete aggregate, or RCA, produced by crushing old concrete structures into new coarse aggregate. But engineers have long faced a stubborn problem: beams made with RCA behave differently from conventional concrete beams, and the world’s leading design codes simply do not capture those differences reliably when it comes to shear failure. A new study published in Neural Computing and Applications suggests that artificial intelligence may finally close that gap.

The research, conducted by Ghazi Bahroz Jumaa of the Civil Engineering Department at the University of Garmian in the Kurdistan Region of Iraq, tackles one of the most safety-critical questions in reinforced concrete design: how much shear force can a beam resist before it fails catastrophically? Shear failure is notoriously brittle and sudden, unlike the gradual, warning-giving flexural failure that designers prefer to govern. For beams without stirrups, the closed steel hoops that normally provide shear reinforcement, the entire resistance must come from the concrete itself, making accurate prediction of that resistance a matter of structural life and death.

The heart of the problem lies in the messy, heterogeneous nature of RCA. When old concrete is crushed, each recycled particle is a composite: natural stone wrapped in a layer of adhered mortar that is often weaker, more porous, and riddled with micro-cracks from the crushing process. In a loaded beam, diagonal cracks propagate through the concrete web, and the rough crack faces resist sliding through a mechanism engineers call aggregate interlock. Weaker, more irregular recycled particles interlock less effectively, and the old mortar attached to them introduces additional weak zones. The result is a material whose shear behavior is inherently more variable than that of natural-aggregate concrete, and whose performance depends on the percentage of recycled content, a variable that most established design equations were never formulated to include.

Existing design provisions, such as those in the American Concrete Institute’s ACI 318 code and Europe’s Eurocode 2, were calibrated decades ago on natural-aggregate concrete. When applied to RCA beams, they can be either unconservative or wastefully conservative, and the experimental literature over the past two decades, from studies by Han and colleagues in 2001 through work by Rahal and Alrefaei, Ignjatović and co-workers, and many others, has documented a wide scatter in measured shear strengths. Empirical equations such as the classic Zsutty formulation, dating back to 1971, capture broad trends but struggle with the added complexity of recycled content. This is precisely the kind of nonlinear, multi-variable prediction problem where machine learning has begun to shine.

Jumaa assembled a database of 179 experimental RCA beam specimens drawn from published laboratory studies spanning more than two decades. Each specimen was characterized by the key parameters that govern shear capacity: beam width, effective depth, the shear span-to-depth ratio, the longitudinal reinforcement ratio, the concrete compressive strength, and, crucially, the percentage of recycled concrete aggregate replacing natural stone. The measured shear capacities ranged from small laboratory beams failing at around 12 kilonewtons to massive 400-millimetre-wide specimens carrying more than 860 kilonewtons, giving the models a rich and demanding training landscape.

Five machine learning algorithms were trained and compared under a rigorous validation protocol: k-Nearest Neighbors, Random Forest, Support Vector Machines, Gaussian Process Regression, and Artificial Neural Networks. Rather than relying on a single train-test split, which can flatter a model by luck of the draw, the study employed a 10-fold cross-validation scheme repeated 20 times. In this procedure the data are repeatedly shuffled and divided into ten subsets; the model trains on nine and is tested on the held-out tenth, with the process cycled through all folds and then repeated from scratch twenty times to average out randomness. This is the gold standard for demonstrating that a model’s accuracy reflects genuine learning of underlying physics rather than memorization of individual specimens.

The results were striking. Support Vector Machines emerged as the top performer, achieving a coefficient of determination of 0.933, meaning the model explained more than 93 percent of the variance in measured shear strength, with a root mean square error of 29.98 kilonewtons and a mean absolute error of just 13.08 kilonewtons. Gaussian Process Regression followed closely with an R-squared of 0.912 and the lowest mean absolute error of the trio at 11.81 kilonewtons, while Artificial Neural Networks achieved an R-squared of 0.892. All three outperformed both the newly proposed empirical equations, including a modified Zsutty equation and an ACI 318-style equation incorporating RCA content, and the traditional design codes themselves. Support Vector Machines, which work by mapping inputs into a high-dimensional feature space where a maximum-margin boundary separates the data, proved especially adept at handling the nonlinear interactions between beam geometry, material strength, and recycled content.

Beyond raw accuracy, the study included a parametric analysis that interrogated how the trained models respond when individual variables change, a crucial sanity check for any data-driven engineering tool. The machine learning predictions reproduced physically sensible trends: shear capacity increased with beam depth and width, and decreased as the recycled aggregate content rose, consistent with the known degradation of aggregate interlock in RCA concrete. This alignment between statistical learning and structural mechanics matters enormously, because a black-box model that contradicts physical intuition would be dangerous to trust, no matter how impressive its error metrics appear on paper.

The practical implications reach well beyond the laboratory. As governments worldwide push circular-economy mandates and green building standards, the volume of RCA entering structural applications is set to grow sharply, and the fib Model Code 2020 and the next generation of Eurocode 2 are actively working toward codified design rules for recycled aggregate concrete structures. Reliable predictive tools are the prerequisite for such codification: engineers need confidence that a beam designed with, say, 50 or 100 percent recycled coarse aggregate will carry its intended shear load with the same margin of safety as a conventional beam. A validated machine learning model, trained on nearly two hundred real experiments, offers exactly that confidence, and could inform both code committees drafting new provisions and practitioners evaluating existing structures built with sustainable materials.

The study also fits into a broader movement sweeping through structural engineering, in which data-driven methods complement, and sometimes surpass, hand-derived empirical formulas. Researchers have already applied neural networks and related techniques to predict the shear capacity of fiber-reinforced polymer concrete beams, the compressive strength of lightweight and recycled concretes, and bond strength in reinforced concrete members. What distinguishes the present work is its focus on the specific, under-addressed case of stirrup-free RCA beams and its head-to-head comparison of five algorithms under a demanding cross-validation protocol. As Jumaa notes in the paper, the work represents a step forward in using machine learning to enhance structural engineering, with the potential to make a significant impact in the field of sustainable materials. If the construction industry is to recycle its own waste at scale, it may well need algorithms, as much as new mix designs, to make the resulting structures safe.

Subject of Research: Machine learning prediction of shear strength in reinforced recycled aggregate concrete beams without stirrups

Article Title: Shear strength prediction of reinforced recycled aggregate concrete beams without stirrups using soft computing and empirical methods

Article References: Jumaa, G. B. (2026). Shear strength prediction of reinforced recycled aggregate concrete beams without stirrups using soft computing and empirical methods. Neural Computing and Applications, 38(19), Article 771. https://doi.org/10.1007/s00521-026-12488-z

Image Credits: AI Generated

DOI: 10.1007/s00521-026-12488-z

Keywords: machine learning, recycled concrete aggregate, shear strength, reinforced concrete beams, stirrups, Support Vector Machines, artificial neural networks, Gaussian Process Regression, ACI 318, Eurocode 2, sustainable construction, structural engineering