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UHPC – Hot Topics, Part 2 of 3

Sunday, October 17, 2021  1:00 PM - 3:00 PM

In the last decade ultra-high-performance concrete (UHPC) has significantly attracted the interest of researchers and practitioners due to its material performance and its potential for innovative practical applications. It has become one of the most influential materials in the construction industry in the twenty-first century. After years of innovations in material, structural and architectural design what are the current UHPC hot topics? This session will invite national and international research groups, material suppliers and contractors to share their knowledge of hot topics of UHPC.
Learning Objectives:
(1) Identify non-proprietary UHPC mixtures;
(2) Discuss the application potential for non-proprietary UHPC;
(3) Review the opportunity of machine learning in material development;
(4) Report on corrosion and chloride diffusion in UHPC elements.

This session has been approved by AIA and ICC for 2 PDHs (0.2 CEUs). Please note: You must attend the live session for the entire duration to receive credit. On-demand sessions do not qualify for PDH/CEU credit.

Development of Non-Proprietary UHPC for Precast Applications

Presented By: John Lawler
Affiliation: Wiss, Janney, Elstner Associates, Inc.
Description: Despite the highly desirable performance characteristics of UHPC, which include post-cracking tensile strength and ductility and exceptional long-term durability in aggressive environments, applications of UHPC in the United States have been limited. The high cost of proprietary UHPC materials, technical challenges associated with production, and a shortage of national design guidelines have all contributed to limited implementation. To foster the implementation of UHPC, particularly in precast concrete bridge and building applications, the Precast/Prestressed Concrete Institute (PCI) funded a research project titled “Implementation of Ultra-High-Performance Concrete in Long-Span Precast Pretensioned Elements for Concrete Buildings and Bridges”. This project has sought to promote implementation of UHPC through the publication of guidelines for developing and producing cost-effective UHPC mixtures, a guide specification for UHPC materials qualification and acceptance, and structural design guidelines. This presentation will summarize the mixture development, production, and characterization of non-proprietary UHPC mixtures based on local materials undertaken with six precasters as part of this research effort, and discuss lessons learned and best practices for implementing UHPC in precast production facilities.

Development of UHPC for the New England Area

Presented By: Kay Wille
Affiliation: University of Connecticut
Description: This presentation shares the development of a cost-efficient, non-proprietary ultra-high-performance concrete using local available materials from the New England area. Methods of investigation include spread test, compression, and direct tensile tests, as well as surface resistivity, and strength development.

Non-Proprietary Ultra-High-Performance Concrete for Precast Bridge Decks Field Joints

Presented By: Mohamed Moustafa
Affiliation: University of Nevada, Reno
Description: Using full-depth precast deck panels and deck bulb tee girders (DBTs) is one of the most popular and reliable solutions nowadays for accelerated bridge construction (ABC). Precast panels and DBTs usually require to be connected on-site using robust materials such as ultra-high-performance concrete (UHPC) for the field joints. The unparalleled mechanical properties of UHPC mixes have gained material popularity for ABC connections. The objective of this study is to leverage readily developed non-proprietary UHPC mixes but using local materials available in the western United States, and proof-test it in precast deck panels transverse field joints and DBTs longitudinal field joints. The presentation provides an overview of a comprehensive experimental program where four full-scale specimens of two representative bridge deck systems with UHPC field joints were tested under static vertical loading. The structural behavior of all specimens was evaluated in terms of load and deflection capacities as well as the field joint performance.

Engineered Ultra High Performance Cement-Based Composites with Low Quantities of Supplementary Cementitious Materials

Presented By: Konstantin Sobolev
Affiliation: University of Wisconsin
Description: Ultra-high-performance concrete (UHPC) is a promising solution to the modern construction, due to the favorable characteristics such as high durability, which can reduce the costs associated to the maintenance. UHPC requires the incorporation of high quantities of supplementary cementitious materials (SCMs) which can increase the production cost. In this study, a new type of fiber reinforced cement -based composite will be explored which indicated similar characteristics to conventional UHPC. The new material contains SCMs such as silica fume or metakaolin, as low as 1% by mass of cementitious materials. The proposed material also includes low dosage of aluminum nano fibers, and high-density polyethylene or polyvinyl alcohol macro fibers. The nanofibers can have a seeding effect to promote the nucleation of C-S-H globules and provide a denser matrix. The incorporation of microfibers into this ultra-high strength cement-based composite will result in a compressive strength of up to 160 MPa, exceeding the benchmark of UHPC.

Using Machine Learning and the Taguchi Method to Generate Performance Density Diagrams in a Multi Scale Mixture Design Approach for UHPC

Presented By: Cesario Tavares
Affiliation: Texas A&M University
Description: The compressive strength of Ultra-High-Performance Concrete (UHPC) is known to be mainly a function of the type, fraction, and quality of the raw materials used for its production. The amount of ingredients required to produce UHPC materials along with the synergistic relationship between them in dictating material performance makes linear regression a very ineffective tool for optimization purposes. Meanwhile, machine learning techniques have been gaining momentum in optimization studies and prediction models. However, the effectiveness of algorithms generated with this technique depend on the size, distribution, and quality of the training data. This study consists in using the Taguchi method to produce a strategic framework for experimental data collection. The experimental data are then used to generate and train a machine learning algorithm that estimates the compressive strength of several combinations of mix proportions within the range of the material contents produced experimentally. The generated matrix of predictors and outcomes is then used to produce a new tool described as Performance Density Diagrams. These diagrams intend to serve as a decision-making aid to be used during the mix design phase, in which performance, durability and sustainability of different mix options can be evaluated simultaneously. In this study, the performance goal consisted of maximizing the compressive strength of the material, while minimizing the porosity levels aiming to improve durability. These diagrams exhibit regions with different strength levels, which allows one to identify multiple proportioning combinations that achieve the desired strength level while simultaneously maximizing the cement replacement with waste by-product powders, aiming to reduce CO2 emissions by designing a greener product.

A Machine Learning Based Framework for Predicting Flowability, Mechanical Properties, and Porosity of Ultra-High-Performance Concrete (UHPC)

Presented By: Soroush Mahjoubi
Affiliation: Stevens Institute of Technology
Description: Ultra-high-performance concrete (UHPC) features exceptional mechanical properties and long-term durability. This study proposes a machine learning-based framework to use mix design variables to predict the compressive strength, flexural strength, flowability, and porosity of UHPC. The framework includes the following steps: (1) form an extensive database by combining structured and unstructured test data in the literature; (2) select a machine learning model according to prediction performance; (3) improve the database by identifying anomalies and irrelevant data; and (4) train and test the machine learning model using the improved database. Specifically, this study collects 1,228 data covering 21 design variables as input and four properties as output; 14 machine learning models are compared in terms of prediction accuracy; then, hyperparameter tuning is applied using tree-structured Parzen estimator with k-fold cross-validation, and a light gradient boosted machine (LightGBM) is selected; next, the isolation forest, an unsupervised anomaly detection technique, is utilized to detect anomalies in the database, and a novel feature selection method based on the mutual information and univariate linear regression are used to eliminate irrelevant input variables; finally, an auto-tuned LightGBM integrating unsupervised anomaly and automatic feature selection is developed and applied to predict the key properties of UHPC. The results indicate that the developed method achieves higher prediction accuracy than the existing methods. With the incorporation of feature importance and Shapley additive explanation, the developed method is used to investigate the significance of individual and interaction effects of mix design variables on the properties of UHPC.

Experimental Behavior and Numerical Modeling of Reinforcement

Presented By: Matthew Bandelt
Affiliation: New Jersey Institute of Technology
Description: Ultra-high-performance concrete is a class of cementitious materials that is characterized by its high strength, ductility, and durability in comparison to conventional concrete. UHPC has a dense matrix and an ability to restrain crack widths, due to the inclusion of fibers, which has limited chloride ingress and resulting corrosion in many isolated accelerated testing environments. Further work is needed to evaluate the service life of these materials under combined mechanical loading and environmental conditioning, especially in long-term testing environments. In this presentation, the results of long-term chloride ponding experiments of reinforced UHPC beams are reported under uncracked and pre-cracked conditions. Numerical simulations using three-dimensional multi-physics simulation techniques are also combined with the experiments to simulate deterioration processes and predict service-life response.

Upper Level Sponsors

Brasfield Gorrie
Concrete Sealants, Inc.
Metromont Corporation
Thomas Concrete

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