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Showing 1-5 of 128 Abstracts search results

Document: 

SP-354_29

Date: 

July 1, 2022

Author(s):

Mario Collepardi, Silvia Collepardi, Giuseppe Marchese and Roberto Troli

Publication:

Symposium Papers

Volume:

354

Abstract:

A special concrete was used to erect the MAXXI building in Rome designed by Zaha Hadid and her team with long, inclined, curvilinear walls. Due to the very congested reinforcements, the original concrete issued by Zaha Hadid and her team was self-compacting concrete (SCC). However, irregular cracks -caused by the restrained drying shrinkage- appeared on the surface of this concrete a few days after removing the formworks. On the other hand, due to aesthetic reasons, neither saw cuts in the hardened concrete to produce regular contraction joints -carried out to avoid the irregular cracks caused by a restrained drying shrinkage- were accepted by the Architects. Therefore, a special 3-SC mixture was developed and used; it is characterized to be: - a self-compacting concrete based on the use of an acrylic superplasticizer, a viscosity modifier to avoid the bleeding risk, and a special particle size distribution of the aggregates; - a self-compressive concrete due to the use of a CaO-based expansive agent; - a self-curing concrete based on the use of a shrinkage-reducing admixture (SRA). This concrete called 3-SC, because it is 3 times “self”, was very successful in producing a crack-free concrete surface even in the very long, curvilinear, and inclined walls: after 18 years of building the long, inclined, curvilinear walls of the MAXXI museum have been carefully examined and during the last inspection their surface resulted to be still sound and crack-free. However, just before the building’s inauguration in 2009, in very few areas some micro-cracks were observed on the concrete surface and considered to be dangerous for the future of the building. Therefore, the concrete surface was treated with a transparent varnish in order to avoid the ingress of the aggressive humid air to protect the steel reinforcements from the corrosion promoted by the carbonation process.

DOI:

10.14359/51736085


Document: 

SP-355_43

Date: 

July 1, 2022

Author(s):

Christian Paglia, Albert Jornet

Publication:

Symposium Papers

Volume:

355

Abstract:

The conservation state of foundation piles of highway viaducts close to a train line was investigated with a visual inspection, laboratory tests on the cementitious material and electrochemical monitoring, as well as galvanostatic pulse measurements for the steel parts. Each viaduct pile had 10 to 15 foundation piles inserted into the ground to a depth down to 15 meters. Two main types of piles were observed. Reinforced concrete piles and steel piles were embedded internally and externally in the cementitious material. The results indicated the absence of significant corrosion of the metals in the upper part of the piles. This was also due to poor carbonation in the ground. Along a viaduct, the presence of chloride in the groundwater increased the risk of corrosion, although it did not reach the steel parts yet. The monitoring of the stray currents did not exhibit a relevant shift in the anodic direction of the steel corrosion potential, thus indicating a reduced corrosion risk. The galvanostatic pulse measurements showed some possible local corrosion issues that may arise, especially with depth. This also depended on the formation of macroelements along the piles. Nevertheless, this latter problem may be reduced due to the higher presence of humidity and the oxygen depletion with depth.

DOI:

10.14359/51736055


Document: 

SP-355_13

Date: 

July 1, 2022

Author(s):

Francesca Tittarelli, Alessandra Mobili, Paolo Chiariotti, Gloria Cosoli, Nicola Giulietti, Alberto Belli, Giuseppe Pandarese, Tiziano Bellezze, Gian Marco Revel

Publication:

Symposium Papers

Volume:

355

Abstract:

To guarantee concrete infrastructure functionality over time inspection and maintenance interventions are required. These inspections are typically scheduled on a periodic basis but may not be sufficient to prevent the onset of deterioration. When these problems occur, extraordinary maintenance operations shall be carried out, causing inconvenience to users and additional costs. The continuous monitoring of the infrastructures allows the transition from programmatic maintenance to predictive maintenance strategies, with a consequent increase in the safety of the structures as well as a reduction in management costs. This work aims to provide a brief overview of continuous monitoring systems for concrete structures developed by Università Politecnica delle Marche, focusing in particular on methods based on free corrosion potential measurement and electrical impedance spectroscopy in the so-called “self-sensing” concrete. The “self-sensing” characteristic of concretes can be improved through conductive additions such as fillers and fibers. The study conducted within the H2020 EU project EnDurCrete has demonstrated how expensive and sometimes toxic commercial conductive carbon-based additions, can be replaced by low-cost, non-toxic industrial by-products, enabling to perform relatively cheap and sustainable continuous monitoring of structures.

DOI:

10.14359/51736019


Document: 

SP-352_02

Date: 

May 31, 2022

Author(s):

Sebastián Castellanos-Toro, Diana Millán, Albert R. Ortiz, Johannio Marulanda, Peter Thomson, Eva O.L. Lantsoght

Publication:

Symposium Papers

Volume:

352

Abstract:

In this study, a prestressed concrete girder bridge without plans and with severe levels of deterioration, located in Cali, Colombia, was load-tested to quantify, experimentally, its live-load behavior. The bridge consists of seven prestressed I-girders covered with a reinforced concrete deck, and four diaphragm beams. A geometric survey was performed to obtain the dimensions for a shell-based linear finite-element model (FEM) representing the bridge superstructure. In this survey, it was observed that the diaphragm beams in the span are geometrically inadequate to contribute to the structural system. Based on the experimental modal properties and the design regulations enforced at the time of bridge design and construction, a first update was made. Modifying the effective stiffness of selected elements to model girder deterioration, a second update was performed based on strain-gauge data from three load tests and visual inspection (VI) of the elements. The three models (basic, modal updated, and load-test/VI updated) were compared with the load distribution factor (DF) obtained from the load test and AASHTO distribution factor estimations. Visual inspection, dynamic characterization, and load testing response of this structure indicated severe deterioration of the girders and the absence of the effect of the diaphragms in the overall structural behavior. The results show that the AASHTO recommendations overestimate the LDF in comparison with the FEM without girder deterioration. When girder deterioration is included in the model, the LDFs change drastically, showing that AASHTO estimations are not in line with the experimental results. As such, for cases of bridges with severe levels of deterioration, it is recommended to use field data to estimate the distribution factors.

DOI:

10.14359/51734854


Document: 

SP-351_06

Date: 

April 1, 2022

Author(s):

Junwon Seo, Euiseok Jeong, and James P. Wacker

Publication:

Symposium Papers

Volume:

351

Abstract:

This paper proposes that Unmanned Aerial System (UAS) technologies integrated with image visibility enhancement algorithms and machine learning are an efficient yet supplementary concrete bridge inspection tool. Two different image enhancement algorithms, i.e., denoise algorithm and image property adjustment, were considered in this study. To assess the adequacy of the proposed UAS technologies in the bridge inspections, the technologies were applied to identify and quantify defects on an existing concrete double-tee bridge located in the state of South Dakota using a Matrice 210 unit. During the inspections, Matrice 210 recorded videos to extract numerous UAS inspection images throughout the bridge. Machine learning was applied to categorize each of the UAS inspection images into certain defect types such as rust and spalling. The denoise algorithm was used to reduce the noise on the categorized defect images based on the pretrained denoising neural network, while the image property adjustment algorithm was employed to improve the visibility of the images by filtering the images’ brightness, contrast, and sharpness. Through these algorithms, defects on the filtered images initially presented with low visibility, were detected. Furthermore, quantification of the defects was able to be completed using pixel-based image analysis with the filtered images. From the UAS-assisted inspections, concrete spalling and rust on railings of the bridge were observed, detected, and quantified successfully. The quantification of spalling showed only a 6.00% difference compared against the inspection report data provided by the South Dakota Department of Transportation (SDDOT).

DOI:

10.14359/51734676


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