Abstract
This study presents a random shape aggregate model by establishing a functional mixture model for images of aggregate shapes. The mesoscale simulation to consider heterogeneous properties concrete is the highly cost- and time-effective method to predict the mechanical behavior of the concrete. Due to the significance of the design of the mesoscale concrete model, the shape of the aggregate is the most important parameter to obtain a reliable simulation result. We propose image analysis and functional data clustering for random shape aggregate models (IFAM). This novel technique learns the morphological characteristics of aggregates using images of real aggregates as inputs. IFAM provides random aggregates across a broad range of heterogeneous shapes using samples drawn from the estimated functional mixture model as outputs. Our learning algorithm is fully automated and allows flexible learning of the complex characteristics. Therefore, unlike similar studies, IFAM does not require users to perform time-consuming tuning on their model to provide realistic aggregate morphology. Using comparative studies, we demonstrate the random aggregate structures constructed by IFAM achieve close similarities to real aggregates in an inhomogeneous concrete medium. Thanks to our fully data-driven method, users can choose their own libraries of real aggregates for the training of the model and generate random aggregates with high similarities to the target libraries.
Highlights
Concrete is the most widely used construction material in the world
The The obtained signature thereal realaggregate aggregate shape are processed to random shape aggregate model (RSAM), create as RSAM, obtained signaturecurves curves from from the shape are processed to create as briefly described detailsofofthe thestatistical statistical functional mixture model, RSAM
Conceptual process and functional functionaldata data clustering random aggregate models (IFAM): (a) all signature curves obtained by digital image processing (DIP), aggregate models (IFAM)
Summary
Concrete is the most widely used construction material in the world. The evaluation of concrete structure is often necessary after the concrete has hardened to determine the structural performance and usability. The digital image-based approach can create the concrete mesostructure with high accuracy by generally using the scanned internal composite to capture a real aggregate image (e.g., X-ray computed tomography [5]). The polygon models cannot emulate the convexity of the crushed aggregates realistically due to its limited data points To address these challenges, the recent study uses a combined digital image-based technique and parameterization method to extract the shape of the aggregates. We propose the image analysis and functional data clustering for RSAM (IFAM) This novel method precisely captures the morphological characteristics of real aggregates to facilitate the simulation of random aggregate shapes. IFAM learns morphological characteristics of aggregates from a library of real aggregates It takes images of aggregates as inputs and converts them to signature curves.
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