Bond Behavior between Steel Fiber Reinforced Polymer (SRP) and Concrete

Steel fiber reinforced polymer (SRP) composite materials, which consist of continuous unidirectional steel wires (cords) embedded in a polymeric matrix, have recently emerged as an effective solution for strengthening of reinforced concrete (RC) structures. SRP is bonded to the surface of RC structures by the same matrix to provide external reinforcement. Interfacial debonding between the SRP and concrete is a primary concern in this type of application. This study aimed to investigate the bond characteristics between SRP and concrete determined by single-lap direct shear tests with different composite bonded lengths and fiber sheet densities (cord spacings). Specimens with medium density fibers failed mainly due to composite debonding, whereas those with low density fibers failed due to fiber rupture. Results of specimens that exhibited debonding were used to determine the bond-slip relationship of the SRP-concrete interface and to predict the full-range load response, which was in good agreement with the experimental results. A database of SRP-concrete direct shear tests reported in the literature was also established. Four analytical equations derived for fiber reinforced polymer (FRP)-concrete debonding were evaluated based on the database results and were found to predict the maximum load within approximately 15% error on average, however, they all underestimated the effective bond length

A High-Temperature-Resistant Inorganic Matrix for Concrete Structures Enhanced by Fiber-Reinforced Polymer

This paper probed deep into a high-temperature resistant inorganic matrix: alkali-activated slag cement (AASC), which is a kind of cementitious material prepared by alkali-activator and pozzolanic or latent hydraulic material. Firstly, the mix ratio of the AASC was optimized to improve the wettability and mechanical properties. Then, the effects of the adhesive matrix and the type of fiber-reinforced polymer (FRP) were observed through FRP-to-concrete bond tests on 93 specimens. The test results, coupled with anchorage analysis, indicate that the AASC has comparable reinforcing effects as those of organic epoxy matrix; the anchorage length of FRP sheets has a significant influence on the failure behavior and failure mode of FRP-enhanced concrete structures. In addition, our tests prove that the AASC has favorable high-temperature resistance and bonding effects. The research results provide a good reference for the design and application of inorganic matrix for FRP-enhancement of concrete structures

A Spike-shaped Anchorage For Steel Reinforced Polymer (SRP)-strengthened Concrete Structures

Steel reinforced polymer (SRP) composite has recently emerged as an effective and economical solution for strengthening of reinforced concrete (RC) structures. Premature debonding failure of unanchored SRP at low load levels generally governs the performance of RC structures strengthened with externally bonded SRP. Therefore, a novel yet simple spike-shaped anchorage system was proposed in this study to prevent the debonding failure of SRP and to improve the interfacial shear capacity. Experimental investigation through single-lap shear tests of SRP-concrete joints showed that the anchorage system changed the failure mode from composite debonding to fiber rupture. In addition, the anchorage system substantially increased the peak load and reduced the interfacial slippage of the SRP-concrete joint compared to the unanchored condition. A numerical procedure based on the finite difference method was developed to predict the full-range load response, and results matched well with the full-range experimental responses of anchored and unanchored specimens. Parametric study of the test results and numerical simulation based on finite difference method both showed that the fiber rupture failure mode could be achieved for anchors in various positions along the bonded length. The closer the anchor is to the loaded end, the less global slip was obtained when the load reached the peak value

Deep Reinforcement Learning for Approximate Policy Iteration: Convergence Analysis and a Post-Earthquake Disaster Response Case Study

Approximate Policy Iteration (API) is a Class of Reinforcement Learning (RL) Algorithms that Seek to Solve the Long-Run Discounted Reward Markov Decision Process (MDP), Via the Policy Iteration Paradigm, Without Learning the Transition Model in the Underlying Bellman Equation. Unfortunately, These Algorithms Suffer from a Defect Known as Chattering in Which the Solution (Policy) Delivered in Each Iteration of the Algorithm Oscillates between Improved and Worsened Policies, Leading to Sub-Optimal Behavior. Two Causes for This that Have Been Traced to the Crucial Policy Improvement Step Are: (I) the Inaccuracies in the Policy Improvement Function and (Ii) the Exploration/exploitation Tradeoff Integral to This Step, Which Generates Variability in Performance. Both of These Defects Are Amplified by Simulation Noise. Deep RL Belongs to a Newer Class of Algorithms in Which the Resolution of the Learning Process is Refined Via Mechanisms Such as Experience Replay And/or Deep Neural Networks for Improved Performance. in This Paper, a New Deep Learning Approach is Developed for API Which Employs a More Accurate Policy Improvement Function, Via an Enhanced Resolution Bellman Equation, Thereby Reducing Chattering and Eliminating the Need for Exploration in the Policy Improvement Step. Versions of the New Algorithm for Both the Long-Run Discounted MDP and Semi-MDP Are Presented. Convergence Properties of the New Algorithm Are Studied Mathematically, and a Post-Earthquake Disaster Response Case Study is Employed to Demonstrate Numerically the Algorithm\u27s Efficacy

A Deep Learning-Informed Design Scheme For Shear Friction At Concrete-to-Concrete Interface: Recommendations For Inclusion In AASHTO LRFD Guidelines

Recent advancements in construction technology have led to high-strength concrete and steel. However, these developments have depreciated the accuracy of the design equations in current provisions, which were based on normal-grade materials. To fill such a research gap, this study presents a novel deep learning-based computation scheme that can replace the current design provisions by virtue of its superior accuracy and reliability. The proposed approach exploits Neural Additive Models (NAMs) in which geometric and material properties associated with a normal weight concrete-to-concrete shear interface are inputted to individual neural network blocks. The outputs of the individual blocks are linearly combined to produce the prediction for interfacial shear strength. This model provides a way to identify and quantify the individual contributions of the input parameters, thus enhancing the interpretability of the model predictions for shear strength at the normal weight concrete-to-concrete interface. The deep learning-informed design (LID) scheme improves the prediction accuracy of the shear strength equation in the existing AASHTO LRFD Bridge Design Specifications by over 32%

An Assessment of Concrete over Asphalt Pavement using Both the Ultrasonic Surface Wave and Impact Echo Techniques

A portable seismic property analyzer (PSPA) was used to simultaneously acquire both ultrasonic surface wave (PSPA-USW) and impact-echo (PSPA-IE) data at predetermined locations along a section of multi-layered pavement. The pavement consisted of a basal concrete layer (~220 mm), an intervening layer of hot-mix asphalt (~60 mm), and a concrete overlay (~220 mm). The section of multi-layered pavement was cored at multiple PSPA test locations for verification purposes. The conditions of the extracted cores were assessed visually, and the static elastic modulus, as well as the compressional wave velocity of each concrete overlay core, were measured in the laboratory. Results from this study demonstrated that the PSPA-USW tool can be used to evaluate the conditions of concrete overlay, the interlayer (hot-mix asphalt), and their bonding conditions from a qualitative perspective. A good correlation between the static and laboratory dynamic modulus from core specimens of concrete overlay were confirmed based on laboratory testing results. However, the field dynamic modulus of core specimens, based on PSPA-USW tests, was lower than both the static modulus and laboratory dynamic modulus. Furthermore, the PSPAIE tool was not able to estimate the depth of the entire pavement and to various pavement layer interfaces due to the interference of flexural mode vibration. Fortunately, the difference between intact and deteriorated pavement can be qualitatively identified from the amplitude spectrum. More core specimens are needed for further studies in order to verify the performance of both PSPAUSW and PSPA-IE techniques for multi-layered pavement condition assessment

MoDOT Pavement Preservation Research Program

The following report documents a research project on pavement preservation performed by the Missouri University of Science and Technology (Missouri S&T) and the University of Missouri-Columbia (UMC) on behalf of the Missouri Department of Transportation (MoDOT). the report consists of a Summary Report followed by six detailed technical reports. to achieve the goal of reducing maintenance costs and improving minor road ratings, MoDOT has embarked upon a plan of formalizing its maintenance/preservation planning. to assist in developing the plan, MoDOT contracted with the Missouri S&T and UMC to conduct a research project, entitled MoDOT Pavement Preservation Research Program . the product of this research would become a part of MoDOT’s overall Pavement Management System. the overall objective of the research was to provide a process that would allow MoDOT to do more selective planning, better engineering and more effective maintenance to minimize costs while maintaining adequate safety and performance of Missouri’s pavements. Six Guidance Documents were to ultimately be created which would act as guidelines for MoDOT’s Pavement Specialists and Engineers. the work was divided into six Tasks, each with its own research team

Welcome to First Day of Poster Session

Introduction of Judges: Poster Session Chair Dr. Lesley Sneed, Professor, Department of Civil, Architectural and Environmental Engineering, Missouri S&T; Dr. Sreenivas Alampalli, Director, Structures Evaluation Services Bureau, NY DOT; Bill DuVall, State Bridge Engineer, Georgia DOT