Skip to content
#diffusion models Open access

Time-Dependent Seismic Performance Evaluation of Precast Concrete Frame Joints Affected by Chloride Ion Corrosion in Coastal Atmospheric Environments

Sep 2026 · Applied Sciences · 16 references
Concrete Corrosion and Durability

Abstract

Precast concrete frame joints in coastal atmospheric environments are susceptible to mechanical performance degradation caused by chloride-induced corrosion, yet joint-scale numerical studies incorporating multi-indicator time-dependent mechanical responses remain limited. This paper presents a coupled framework that integrates chloride diffusion, corrosion, and finite-element analysis for a typical precast beam–column joint to evaluate the relative changes in seismic performance indicators across service ages of 0, 15, 30, 40, and 50 years. The numerical model was baseline-validated against uncorroded and corroded test specimens under cyclic loading. Time-dependent models accounting for chloride diffusion, rebar corrosion, and material strength degradation were implemented. The elastic modulus reduction was restricted to the damaged covering concrete rather than the intact internal concrete. The simulation results show that mechanical degradation is limited in the early service stage, whereas hysteretic pinching and deformation-related deterioration become more pronounced with increasing service age. By 50 a, the peak load-bearing capacity has decreased by 13.68%, whereas the ultimate displacement and ductility coefficients have declined by 15.10% and 37.40%, respectively. These results should be interpreted as case-specific predictions under the adopted cover thickness, chloride exposure condition, and material parameters rather than as universal deterioration thresholds. The findings indicate that service-life evaluations of precast joints in coastal atmospheric environments should not rely solely on strength indicators but should also incorporate stiffness, ductility, and energy dissipation capacity.

View source

Similar papers

#computer vision Open access Jun 2016

Software Development in Startup Companies: The Greenfield Startup Model

The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.

Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al. · 178 citations · ⚡14
#computer vision Open access Oct 2016

Software Startups - A Research Agenda

Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.

M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al. · 157 citations · ⚡17
#machine learning Review Open access Oct 2016

“Failures” to be celebrated: an analysis of major pivots of software startups

This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al. · 127 citations · ⚡15
#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Conference Sep 2010

Exploring the Sources of Waste in Kanban Software Development Projects

The application of agile software methods and more recently the integration of Lean practices contribute to the trend of continuous improvement in the software industry. One such area warranting proper empirical evidence is a project’s operational efficiency when using the Kanban method. This short paper takes a new angle and explores waste in the Kanban-driven software development project context. A preliminary research model is presented for helping the consequent replication of the study. The results from the empirical analysis suggest Kanban can be an effective method in visualizing and organizing the current work, but does not prevent waste from creeping in, although the overall project outcome may be successful.

Marko Ikonen, Petri Kettunen, Nilay V. Oza et al. · 67 citations · ⚡9

Related blog posts

MIT News · Artificial Intelligence Sep 14, 2026

New method enables AI for safety-critical situations

The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.

GPT-Lab Sep 10, 2026

Responsible AI Must Consider Its Afterlife

AI may appear weightless, but every model depends on physical infrastructure. To understand responsible AI, we need to look beyond algorithms and consider the entire lifecycle of the hardware behind them. The post Responsible AI Must Consider Its Afterlife appeared first on GPT-Lab.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.