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International Concrete Abstracts Portal

Showing 1-5 of 1051 Abstracts search results

Document: 

SP-350_05

Date: 

November 1, 2021

Author(s):

Salvio A. Almeida Jr. and Serhan Guner

Publication:

Symposium Papers

Volume:

350

Abstract:

Soft computing applications through artificial intelligence (AI) are becoming increasingly popular in civil engineering. From concrete technology to structural engineering, AIs have provided successful solutions to various problems and greatly reduced the computational costs while achieving excellent prediction accuracy. In this study, a review of the main artificial neural network (ANN) types used in civil engineering is presented. Each ANN type is described, and example applications are provided. As a new research contribution, a deep feedforward neural network (FFNN) is developed to predict the load capacities of post-installed adhesive anchors installed in cracked concrete, which is challenging and computationally expensive to achieve with conventional methods. The development of this FFNN is discussed, the influence of several parameters on its performance is demonstrated, and optimum parameter values are selected. In addition, a hybrid methodology that combines 2D nonlinear finite element (NLFE) techniques with the developed FFNN is briefly presented to account for real-life adverse effects in anchor analysis, including concrete cracking, wind-induced beam bending, and elevated temperatures. The results show that the developed network and methodology can rapidly and efficiently predict the load capacities of adhesive anchors installed into cracked concrete, accounting for the damage caused by the cracks, with high accuracies.


Document: 

SP-350_14

Date: 

November 1, 2021

Author(s):

Jung Wang, Chao Liu, and Yail J. Kim

Publication:

Symposium Papers

Volume:

350

Abstract:

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.


Document: 

SP-350_01

Date: 

November 1, 2021

Author(s):

AlaaEldin Abouelleil, Hayder A. Rasheed, and Eric Fletcheri

Publication:

Symposium Papers

Volume:

350

Abstract:

The structural deterioration of aging infrastructure systems is becoming an increasingly important issue worldwide. To compound the problem, economic strains limit the resources available for the repair or replacement of such systems. Over the past several decades, structural health monitoring (SHM) has proven to be a cost-effective method for the detection and evaluation of damage in structures. Visual inspection and condition rating is one of the most commonly applied SHM techniques, but the effectiveness of SHM varies depending on the availability and experience of qualified personnel and largely qualitative damage evaluations. Simply supported three-dimensional reinforced concrete T-beams with varying geometric, material, and cracking properties were modeled using Abaqus finite element (FE) analysis software. Up to five cracks were considered in each beam, and the ratios of stiffness between cracked and healthy beams with the same geometric and material parameters were measured at nine equidistant nodes along the beam. A feedforward ANN utilizing backpropagation learning algorithms was then trained on the FE model database with beam properties and nodal stiffness ratios serving as inputs for the neural network model. The outputs consisted of the predicted parameters of location, depth, and width of up to five cracks. This inverse problem is very difficult or impossible to solve with the training done by the Artificial Neural Network. One ANN was trained to predict the parameters of the cracks using the full database of FE simulations. The damage prediction ANN achieved fair prediction accuracies, with coefficients of determination (R2) equal to 0.42. This result was the outcome of the no uniqueness in the prediction of this inverse analysis. Nevertheless, this ANN model provides a rough estimate of the cracking type and damage content in bridge girders once the nodal stiffness ratios are measured by applying a field vehicle loading and measuring the deflection using a theodolite. A touch-enabled user interface was developed to allow the ANN model to predict the crack configurations. The application was given the acronym DRY BEAM, for Damage Recognition Yielding Bridge Evaluation After Monitoring.


Document: 

SP-349_35

Date: 

April 22, 2021

Author(s):

Alexandre Rodrigue, Josée Duchesne, Benoit Fournier and Benoit Bissonnette

Publication:

Symposium Papers

Volume:

349

Abstract:

Alkali-activated slag/fly ash concretes activated with combined sodium silicate and sodium hydroxide show good mechanical and durability properties in general. When tested in terms of resistance to freezing and thawing cycling in watersaturated conditions, the concretes tested in this study show final values of relative dynamic modulus averaging 100% after 300 cycles. However, all tested concretes showed poor performance towards freezing and thawing in presence of de-icing salts with only one tested mixture showing a final average scaling value below 0.5 kg/m². Early-age microcracking is observed on all tested concretes and is correlated to high values of autogenous shrinkage in equivalent paste mixtures. Increasing the fly ash content reduces both the observed autogenous shrinkage and early-age cracking. Low drying shrinkage values ranging from 470 to 530 μm/m after 448 days of measurements at 50% RH and 23°C are noted. The use of fly ash in these alkali-activated concretes reduces the expansion levels of concrete specimens incorporating alkali-silica reactive aggregates. With increasing fly ash contents (20, 30 and 40% replacement), decreasing expansions are observed for any given reactive aggregate. In general, the durability properties measured in this study were improved by partially substituting slag with fly ash as binder material.


Document: 

SP-349_45

Date: 

April 22, 2021

Author(s):

Bakhta Boukhatem, Ablam Zidol and Arezki Tagnit-Hamou

Publication:

Symposium Papers

Volume:

349

Abstract:

This study presents an accurate corrosion prediction through an intelligent approach based on deep learning. The deep learning is used to predict the time-to-corrosion induced cover cracking in reinforced concrete elements exposed to chlorides ions. The key parameters taken into consideration include thickness, quality and condition of the concrete cover. The prediction performance of the deep learning model is compared against traditional machine learning approaches using neural network and genetic algorithms. Results show that the proposed approach provides better prediction with higher generalization ability. The efficiency of the method is validated by an accelerated corrosion test conducted on 91 and 182-day moist cured reinforced fly ash concrete samples with different water-to-binder ratios. The results are in agreement with the model predictions. They also show that using the proposed model for numerical investigations is very promising, particularly in extracting the effect of fly ash on reducing the extent of corrosion. Such an intelligent prediction will serve as an important input in order to assist in service life prediction of corroding reinforced concrete structures as well as repair evaluation.


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