Bayesian machine learning-aided approach bridges between dynamic elasticity and compressive strength in the cement-based mortars

Physics-informed recurrent neural networks and hyper-parameter optimization for dynamic process systems

(2023) Tuse Asrav et al.   COMPUTERS & CHEMICAL ENGINEERING

Hybrid machine learning model and Shapley additive explanations for compressive strength of sustainable concrete

(2022) Yanqi Wu et al.   CONSTRUCTION AND BUILDING MATERIALS

Accurate prediction of concrete compressive strength based on explainable features using deep learning

(2022) Ziyue Zeng et al.   CONSTRUCTION AND BUILDING MATERIALS

Machine learning-based compressive strength modelling of concrete incorporating waste marble powder

(2022) Elyas Asadi Shamsabadi et al.   CONSTRUCTION AND BUILDING MATERIALS

Evaluating compressive strength of concrete made with recycled concrete aggregates using machine learning approach

(2022) Van Quan Tran et al.   CONSTRUCTION AND BUILDING MATERIALS

Seismic performance assessment of corroded RC columns based on data-driven machine-learning approach

(2022) Ji-Gang Xu et al.   ENGINEERING STRUCTURES

Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users

(2022) Laurent Valentin Jospin et al.   IEEE Computational Intelligence Magazine

Novel hybrid machine learning approach for predicting structural response of RC beams under blast loading

(2022) Monjee K. Almustafa et al.   Structures

Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms

(2022) Vimal Rathakrishnan et al.   Scientific Reports

New SHapley Additive ExPlanations (SHAP) Approach to Evaluate the Raw Materials Interactions of Steel-Fiber-Reinforced Concrete

(2022) Madiha Anjum et al.   Materials

Explainable machine learning for understanding and predicting geometry and defect types in Fe-Ni alloys fabricated by laser metal deposition additive manufacturing

(2022) Jeong Ah Lee et al.   Journal of Materials Research and Technology-JMR&T

Experimental investigation and comparative machine-learning prediction of compressive strength of recycled aggregate concrete

(2021) S. Reza Salimbahrami et al.   Soft Computing

Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment

(2021) Sarmad Dashti Latif   ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH

Geopolymer Recycled Aggregate Concrete: From Experiments to Empirical Models

(2021) Hoai-Bao Le et al.   Materials

Prediction of cement-based mortars compressive strength using machine learning techniques

(2021) Panagiotis G. Asteris et al.   NEURAL COMPUTING & APPLICATIONS

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

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

A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness

(2021) Chen Yang et al.   ACTA MATERIALIA

Building life-span prediction for life cycle assessment and life cycle cost using machine learning: A big data approach

(2021) Sukwon Ji et al.   BUILDING AND ENVIRONMENT

Characterization of nanoscale structural heterogeneity in metallic glasses: A machine learning study

(2021) Majid Samavatian et al.   JOURNAL OF NON-CRYSTALLINE SOLIDS

Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques

(2021) Yue Xu et al.   Materials

Machine learning applications for assessment of dynamic progressive collapse of steel moment frames

(2021) Yan Fei Zhu et al.   Structures

Machine learning applied to the design and inspection of reinforced concrete bridges: Resilient methods and emerging applications

(2021) Weiying Fan et al.   Structures

Prediction of Static Modulus and Compressive Strength of Concrete from Dynamic Modulus Associated with Wave Velocity and Resonance Frequency Using Machine Learning Techniques

(2020) Jong Yil Park et al.   Materials

Assessment of compressive strength of Ultra-high Performance Concrete using deep machine learning techniques

(2020) Omar R. Abuodeh et al.   APPLIED SOFT COMPUTING

Recycling of steel slag aggregate in portland cement concrete: An overview

(2020) Qiao Dong et al.   JOURNAL OF CLEANER PRODUCTION

Correlation-driven machine learning for accelerated reliability assessment of solder joints in electronics

(2020) Vahid Samavatian et al.   Scientific Reports

Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete

(2020) Min-Chang Kang et al.   CONSTRUCTION AND BUILDING MATERIALS

A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications

(2020) Onur Avci et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Revealing the substitution preference of zinc in ordinary Portland cement clinker phases: A study from experiments and DFT calculations

(2020) Jianping Zhu et al.   JOURNAL OF HAZARDOUS MATERIALS

Influence of α-Calcium Sulfate Hemihydrate on Setting, Compressive Strength, and Shrinkage Strain of Cement Mortar

(2019) Bokyeong Lee et al.   Materials

Effects of chemical composition of fly ash on compressive strength of fly ash cement mortar

(2019) Young Keun Cho et al.   CONSTRUCTION AND BUILDING MATERIALS

Mechanical and fracture properties of recycled aggregate concrete in design codes and empirical models

(2019) Elhem Ghorbel et al.   Structural Concrete

Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach

(2019) De-Cheng Feng et al.   CONSTRUCTION AND BUILDING MATERIALS

Influence of Selected Factors on the Relationship between the Dynamic Elastic Modulus and Compressive Strength of Concrete

(2018) Krystian Jurowski et al.   Materials

Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods

(2018) Benjamin A. Young et al.   CEMENT AND CONCRETE RESEARCH

The impact of encoding–decoding schemes and weight normalization in spiking neural networks

(2018) Zhengzhong Liang et al.   NEURAL NETWORKS

Establishing a relationship between modulus of elasticity and compressive strength of recycled aggregate concrete

(2016) Rui Vasco Silva et al.   JOURNAL OF CLEANER PRODUCTION

A fundamental study on compressive strength, static and dynamic elastic moduli of young concrete

(2015) Yong Zhou et al.   CONSTRUCTION AND BUILDING MATERIALS