Unveiling non-steady chloride migration insights through explainable machine learning - XML驱动的XGBoost与SHAP特征解析

Aug 5, 2026

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This study examines how concrete mix ingredients affect the non-steady chloride migration coefficient (Dnssm) using explainable machine learning combining Extreme Gradient Boosting (XGBoost) with Shapley Additive Explanations (SHAP). A literature dataset of 204 observations trains the XGBoost model to predict Dnssm. The approach achieves strong performance, with MAE, RMSE, and R2 reported for both training and test sets. SHAP reveals feature importance and dependencies, highlighting coarse aggregate, superplasticizer, concrete age, cement, and water as top contributors. Visualization of SHAP values supports interpretation, improving trust and aiding development of concrete with better chloride penetration resistance.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages20

Reading Time50 min

AccessFree to download

Introduction

Chloride attack and concrete durability

Chloride transport mechanisms and diffusion-based models

Limitations of laboratory testing

This is a self-archived version of the original publication The self-archived version is a publisher’s pdf of the original publication. Please note that the self-archived version may differ from the original in pagination, typographical details and illustrations. To cite this, use the original publication: Taffese, W. Z. , & Espinosa-Leal, L. (2024) . Unveiling non-steady chloride migration insights through explainable machine learning, Journal of Building Engineering, 82(108370) . DOI: 10. 1016/j.jobe.2023.108370 All material supplied via Arcada’s self-archived publications collection in Theseus repository is protected by copyright laws. Use of all or part of any of the repository collections is permitted only for personal non-commercial, research or educational purposes in digital and print form. You must obtain permission for any other use. Journal of Building Engineering 82 (2024) 108370 Contents lists available at ScienceDirect Journal of Building Engineering journal [homepage:](homepage: www.elsevier.com/locate/jobe)[ www.elsevier.com/locate/jobe](homepage: www.elsevier.com/locate/jobe) | Unveiling non-steady chloride migration insights through explainable machine learning | | | | | --- | --- | --- | --- | | *
Woubishet Zewdu Taffese , Leonardo Espinosa-Leal
School of Research and Graduate Studies, Arcada University of Applied Sciences, Helsinki, Finland | | | | | A R T I C L E I N F O | | A B S T R A C T | | | Keywords:
Model-agnostic explanations SHAP
Chloride diffusion
Concrete
Durability
Explainable machine learning Chloride migration coefficient | | This study explores the influence of concrete mix ingredients on the non-steady chloride migration coefficient (Dnssm) using an explainable machine learning (XML) approach that integrates Extreme Gradient Boosting (XGBoost) and Shapley Additive Explanations (SHAP). The dataset, comprising 204 observations from literature, is utilized to train the XGBoost algorithm for predicting Dnssm. The model demonstrates notable performance metrics with (MAE = 1.61 × 10 − 12 m2/s, RMSE = 2.38 × 10 − 12 m2/s, and R2 = 0.95) in the training set and (MAE = 2.22 × 10 − 12 m2/s, RMSE = 3.18 × 10 − 12 m2/s, and R2 = 0.87) and the test set. The SHAP method provides comprehensive insights into feature importance, offering valuable information about the relationships and dependencies among various features. The top five features identified as significant contributors include coarse aggregate, superplasticizer, concrete age, cement, and water. Visualization of SHAP values through diverse plots proves essential for obtaining a thorough understanding of feature influence. The explainability of the model’s results contributes new insights, aiding in the development of optimal and sustainable concrete with enhanced resistance to chloride penetration. Furthermore, the model’s explainability fosters trust in its predictions, facilitating seamless integration into real-world applications. | | 1. Introduction Concrete is an essential component in the field of civil engineering construction, but its durability is affected by a range offactors as time passes. Among these factors, chloride attack emerges as prominent threat to the durability of reinforced concrete (RC) structures, especially in marine environments or regions subjected to chloride-containing de-icing salts in cold climates [2]. While chloride penetration itself doesn’t harm concrete, once the concentration of chloride ions surpasses a certain threshold at the steel reinforcement bars, it triggers depassivation and subsequent corrosion [3]. The corrosion of reinforcement bars caused by chloride has a detrimental impact on the functionality and safety of RC structures worldwide, leading to significant economic losses stemming from the premature need for rehabilitation and repair. In fact, some developed countries allocate a substantial portion of their gross domestic product (GDP), ran

FAQs

What modeling approach is used to predict the non-steady chloride migration coefficient (Dnssm)?

The study integrates XGBoost with SHAP (Shapley Additive Explanations) within an explainable machine learning framework to predict Dnssm and interpret influential features.

How is the dataset constructed for training the prediction model?

The dataset is built from literature and contains 204 observations, which are used to train the XGBoost algorithm to forecast Dnssm.

Which concrete-related features are identified as the most significant contributors by SHAP?

SHAP identifies coarse aggregate, superplasticizer, concrete age, cement, and water among the top five most important features contributing to Dnssm.

Aug 5, 2026

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