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International Concrete Abstracts Portal

Showing 1-5 of 1120 Abstracts search results

Document: 

SP-351_04

Date: 

April 1, 2022

Author(s):

Nicholas Triandafilou, Mark Guirguis, Ephraim Dissen, Olu Awomolo, and Mustafa Mahamid

Publication:

Symposium Papers

Volume:

351

Abstract:

Fireproofing deterioration is widespread in industrial facilities throughout the country. Spalling concrete has potential to damage equipment and harm personnel. Replacing concrete fireproofing like-in-kind, without consideration for proper anchorage or material durability, does not eliminate the hazard as spalls may potentially occur again over time. However, when properly designed and installed, concrete is a durable option for replacing deficient fireproofing in aggressive environments typically present in industrial processing units. This paper presents the results of a case study on a structure in a Midwest industrial complex. Extensive concrete fireproofing repairs were performed on the structure 12 years ago. Design requirements included normal weight concrete with polypropylene fibers which enhance durability by improving cracking resistance. During a fire, the fibers melt forming relief channels for moisture to escape, thus eliminating explosive spalling. Installation methods included welded wire reinforcement (WWR) with positive anchorage to structural steel. WWR was attached to post-installed adhesive anchors between column flanges where existing fireproofing was sound and difficult to remove. After 12 years in service, repairs exhibit no significant defects. This level of durability is attributed to the design and installation methods utilized. Concrete fireproofing is a durable option for fire protection, provided structures are designed to support its weight, its mixture design is properly proportioned, and it is adequately anchored and reinforced.


Document: 

SP-351_07

Date: 

April 1, 2022

Author(s):

Neal S. Berke and Ali N. Inceefe

Publication:

Symposium Papers

Volume:

351

Abstract:

Major bridges are requiring extended service lives of 100 years or more. This requires the use of high performance concretes and often enhanced corrosion protection provided by improved corrosion resistance of the reinforcing bars by using alloying, coatings, and/or corrosion inhibitors. Producing the entire bridge deck out of high performance concrete can lead to excessive cracking due to autogenous and drying shrinkage. Though this can be reduced by using shrinkage reducing admixtures or lightweight fines, the cost to implement these techniques for a full deck is high. However, a high performance concrete overlay uses considerably less high performance concrete, and as such can reduce the overall cost of the bridge deck and potentially allow for use of a more user friendly, less costly base concrete. This paper models the service life of a bridge deck using a high performance overlay. A probabilistic approach is used and the effect of cracking is included.


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.

DOI:

10.14359/51734321


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.

DOI:

10.14359/51734312


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.

DOI:

10.14359/51734308


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