Towards white box modeling of compressive strength of sustainable ternary cement concrete using explainable artificial intelligence (XAI)

A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks

(2022) Mir Riyanul Islam et al.   Applied Sciences-Basel

Prediction of concrete strengths enabled by missing data imputation and interpretable machine learning

(2022) Gideon A. Lyngdoh et al.   CEMENT & CONCRETE COMPOSITES

Counterfactual explanations and how to find them: literature review and benchmarking

(2022) Riccardo Guidotti   DATA MINING AND KNOWLEDGE DISCOVERY

Machine learning models for predicting resistance of headed studs embedded in concrete

(2022) Mahdi Razavi Setvati et al.   ENGINEERING STRUCTURES

Mapping Research Knowledge on Rice Husk Ash Application in Concrete: A Scientometric Review

(2022) Muhammad Nasir Amin et al.   Materials

Plastic Waste Management Strategies and Their Environmental Aspects: A Scientometric Analysis and Comprehensive Review

(2022) Saimin Huang et al.   International Journal of Environmental Research and Public Health

A Systematic Review of the Research Development on the Application of Machine Learning for Concrete

(2022) Kaffayatullah Khan et al.   Materials

Estimating compressive strength of lightweight foamed concrete using neural, genetic and ensemble machine learning approaches

(2022) Babatunde Abiodun Salami et al.   CEMENT & CONCRETE COMPOSITES

Explainable machine learning models for predicting the axial compression capacity of concrete filled steel tubular columns

(2022) Celal Cakiroglu et al.   CONSTRUCTION AND BUILDING MATERIALS

Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost

(2022) Ziqi Li   COMPUTERS ENVIRONMENT AND URBAN SYSTEMS

Scientometric review of international research trends on thermal energy storage cement based composites via integration of phase change materials from 1993 to 2020

(2021) Sher Afgan et al.   CONSTRUCTION AND BUILDING MATERIALS

An engineer's guide to eXplainable Artificial Intelligence and Interpretable Machine Learning: Navigating causality, forced goodness, and the false perception of inference

(2021) M.Z. Naser   AUTOMATION IN CONSTRUCTION

Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models

(2021) Panagiotis G. Asteris et al.   CEMENT AND CONCRETE RESEARCH

Data-driven model for ternary-blend concrete compressive strength prediction using machine learning approach

(2021) Babatunde Abiodun Salami et al.   CONSTRUCTION AND BUILDING MATERIALS

Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers

(2021) André Artelt et al.   NEUROCOMPUTING

Explainable artificial intelligence: an analytical review

(2021) Plamen P. Angelov et al.   Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

Why fairness cannot be automated: Bridging the gap between EU non-discrimination law and AI

(2021) Sandra Wachter et al.   Computer Law & Security Review

Ensemble learning for the early prediction of neonatal jaundice with genetic features

(2021) Haowen Deng et al.   BMC Medical Informatics and Decision Making

Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond

(2021) Guang Yang et al.   Information Fusion

Perturbation-based methods for explaining deep neural networks: A survey

(2021) Maksims Ivanovs et al.   PATTERN RECOGNITION LETTERS

Soft computing techniques in structural and earthquake engineering: a literature review

(2020) Roberto Falcone et al.   ENGINEERING STRUCTURES

A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model

(2020) Jin Duan et al.   ENGINEERING WITH COMPUTERS

Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and salp swarm algorithm

(2020) Amirreza Kandiri et al.   CONSTRUCTION AND BUILDING MATERIALS

A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength

(2020) Danial Jahed Armaghani et al.   NEURAL COMPUTING & APPLICATIONS

Eco-Mechanical Performance of Binary and Ternary Cement Blends Containing Fly Ash and Slag

(2020) Mohammed KH Radwan et al.   Proceedings of the Institution of Civil Engineers-Engineering Sustainability

Predicting uniaxial compressive strength of oil palm shell concrete using a hybrid artificial intelligence model

(2020) Junfei Zhang et al.   Journal of Building Engineering

Mapping and holistic design of natural hydraulic lime mortars

(2020) Maria Apostolopoulou et al.   CEMENT AND CONCRETE RESEARCH

A historical perspective of explainable Artificial Intelligence

(2020) Roberto Confalonieri et al.   Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

Experimental and ANN analysis of temperature effects on the permanent deformation properties of demolition wastes

(2020) Behnam Ghorbani et al.   Transportation Geotechnics

Stochastic Vulnerability Assessment of Masonry Structures: Concepts, Modeling and Restoration Aspects

(2019) Panagiotis G. Asteris et al.   Applied Sciences-Basel

Using Scopus’s CiteScore for assessing the quality of computer science conferences

(2019) Lokman I. Meho   Journal of Informetrics

Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns

(2019) Payam Sarir et al.   ENGINEERING WITH COMPUTERS

Mapping Knowledge in the Economic Areas of Green Building Using Scientometric Analysis

(2019) Xue Xiao et al.   Energies

Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values

(2019) Raquel Rodríguez-Pérez et al.   JOURNAL OF MEDICINAL CHEMISTRY

Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples

(2019) Lei Sun et al.   ENGINEERING WITH COMPUTERS

Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories

(2019) Jian Zhou et al.   SAFETY SCIENCE

Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series

(2019) Matheus Henrique Dal Molin Ribeiro et al.   APPLIED SOFT COMPUTING

Compressive strength of earth block masonry: Estimation based on neural networks and adaptive network-based fuzzy inference system

(2019) Guanqi Lan et al.   COMPOSITE STRUCTURES

Concrete compressive strength using artificial neural networks

(2019) Panagiotis G. Asteris et al.   NEURAL COMPUTING & APPLICATIONS

A holistic review of cement composites reinforced with graphene oxide

(2018) Yidong Xu et al.   CONSTRUCTION AND BUILDING MATERIALS

A scientometric analysis and visualization of global green building research

(2018) Amos Darko et al.   BUILDING AND ENVIRONMENT

Performance of sustainable concretes containing very high volume Class-F fly ash and ground granulated blast furnace slag

(2017) Aliakbar Gholampour et al.   JOURNAL OF CLEANER PRODUCTION

Random forest for ordinal responses: Prediction and variable selection

(2016) Silke Janitza et al.   COMPUTATIONAL STATISTICS & DATA ANALYSIS

Prediction of the mechanical properties of structural recycled concrete using multivariable regression and genetic programming

(2016) Iris González-Taboada et al.   CONSTRUCTION AND BUILDING MATERIALS

Fuzzy inference system to formulate compressive strength and ultimate strain of square concrete columns wrapped with fiber-reinforced polymer

(2016) Kourosh Nasrollahzadeh et al.   NEURAL COMPUTING & APPLICATIONS

Consistency of random forests

(2015) Erwan Scornet et al.   ANNALS OF STATISTICS

Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches

(2015) Iman Mansouri et al.   COMPOSITES PART B-ENGINEERING

Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation

(2015) Alex Goldstein et al.   JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS

Prediction model for compressive strength of basic concrete mixture using artificial neural networks

(2014) Srđan Kostić et al.   NEURAL COMPUTING & APPLICATIONS

Optimization of existing equations using a new Genetic Programming algorithm: Application to the shear strength of reinforced concrete beams

(2012) Juan L. Pérez et al.   ADVANCES IN ENGINEERING SOFTWARE

Mechanical properties of self consolidating concrete blended with high volumes of fly ash and slag

(2012) Katherine Kuder et al.   CONSTRUCTION AND BUILDING MATERIALS

Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network

(2011) U. Atici   EXPERT SYSTEMS WITH APPLICATIONS

Advances in alternative cementitious binders

(2010) M.C.G. Juenger et al.   CEMENT AND CONCRETE RESEARCH

Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data-Mining Techniques

(2010) Jui-Sheng Chou et al.   JOURNAL OF COMPUTING IN CIVIL ENGINEERING

Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete

(2009) Fatih Özcan et al.   ADVANCES IN ENGINEERING SOFTWARE

Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash

(2009) Mohammed Sonebi et al.   CONSTRUCTION AND BUILDING MATERIALS

Utilization of fly ash with silica fume and properties of Portland cement–fly ash–silica fume concrete

(2009) Thanongsak Nochaiya et al.   FUEL

Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks

(2008) Mustafa Sarıdemir   ADVANCES IN ENGINEERING SOFTWARE

Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks

(2008) Gregor Trtnik et al.   ULTRASONICS