International Concrete Abstracts Portal

With IoT sensors gaining widespread adoption in recent years for monitoring in-situ concrete properties, the volume of data generated using these sensors is growing at a significant rate. These sensors are typically used for several purposes among which temperature, humidity and strength monitoring (using the maturity method) are currently the most common. This data is typically collected at centralized cloud-based databases where they can be accessed by sensor end-users as well as algorithm developers. This work presents on how data from these IoT sensors has been used by the authors to train machine learning algorithms to perform several tasks including but not limited to: detecting anomalies, detecting events in the service life of the sensor (e.g. concrete pouring,) suggesting mixture alterations to optimize performance and predicting future performance. These capabilities are currently being used by concrete practitioners on daily basis. This is done using data collected from tens of thousands of sensors, used in over 7500 projects representing geographical regions of over 45 countries and representing several thousand unique concrete mixtures. This, to the authors’ knowledge, is the largest dataset available for training such algorithms. Two use cases are presented for how this data is utilized to train machine learning algorithms to assist practitioners in day-to-day activities such as mixture optimization.

This paper presents and explains an implementation of artificial intelligence for the real-time crack detection of ultra-high-performance concrete (UHPC). A deep learning algorithm is employed to process image data and to identify physical cracks. The state-of-the-art object detection method generates accurate results with small datasets. To provide training and validation images, UHPC specimens are cast with various fibers and loaded per an ASTM standard, including steel and synthetic (collated and monofilament polypropylene) fibers. After testing, sample images are labeled with an annotation tool and the algorithm is trained and validated with an image recognition approach, leading to a mean average precision (mAP) of 99%. The occurrence of cracking and propagation are linked with the applied load level to appraise the influence of the mixed fibers in the crack development of UHPC. It needs to be noted that the adopted deep learning architecture is incapable of quantifying crack width and area directly; therefore, a Java-based image processing program is used to measure these properties of the specimens. The characteristics of the load-induced cracks are dominated by the fiber types. Plain UHPC fails rapidly and the flexural capacity of UHPC increases with the presence of the fibers; especially, the UHPC with steel fibers demonstrates higher flexural capacities than other cases.

This research employs a novel Bayesian estimation technique to perform model updating on a coupled vehicle-bridge finite element model (FEM) for the purposes of classifying damage on a reinforced concrete bridge. Unlike existing Artificial intelligence (AI) techniques, the proposed methodology makes use of an embedded FEM, thus reducing the parameter space while simultaneously guiding the Bayesian model via physics-based principles. To validate the method, bridge response data is generated from the vehicle-bridge FEM given a set of “true” parameters and the bias and standard deviation of the parameter estimates are analyzed. Additionally, the mean parameter estimates are used to solve the FEM, and the results are compared against results obtained for “true” parameter values. Furthermore, a sensitivity study is conducted to demonstrate methods for properly formulating model spaces to improve the Bayesian estimation routine. The study concludes with a discussion highlighting factors that need to be considered when using experimental data to update vehicle-bridge FEMs with the Bayesian estimation technique.

A computational framework employing machine learning (ML) is applied to predict failure mode of ultra-high-performance concrete (UHPC) beams. For this purpose, results from a number of tests on UHPC beams with different geometric and loading configurations and material characteristics are collected and utilized as an input to the ML framework. Results from numerical studies are not included in the data set due to the fact that they are highly dependent upon the adopted material models, meshing practices, as well as other assumptions used in modeling. Artificial neural network is used to predict the failure mode of the UHPC beams. Results indicate that the proposed ML framework is capable of predicting failure mod of UHPC beams with varying reinforcement and configurations, and can be considered for use in design applications. This paper aims to promote the applicability of ML for a practical engineering problem, detecting structural response of UHPC beams.

The probabilistic study of fire exposed structures is laborious and computationally challenging, especially when using advanced numerical models. Moreover, fragility curves developed through traditional approaches apply only to a particular design (structural detailing, fire scenario). Any alteration in design necessitates the computationally expensive re-evaluation of the fragility curves. Considering the above challenges, the use of surrogate models has been proposed for the probabilistic study of fire exposed structures. Previous contributions have confirmed the potential of surrogate models for developing fragility curves for single structural members including reinforced concrete slabs and columns. Herein, the potential of regression-based surrogate models is investigated further with consideration of structural systems. Specifically, an advanced finite element model for evaluating the fire performance of a composite slab panel acting in tensile membrane action is considered. A surrogate model is developed and used to establish fire fragility curves. The results illustrate the potential of surrogate modeling for probabilistic structural fire design of composite structures.