☆ 4.6 Review

A Systematic Review of Machine Learning Techniques and Applications in Soil Improvement Using Green Materials

SUSTAINABILITY (2023)

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Article Geosciences, Multidisciplinary

Prediction of wall deflection induced by braced excavation in spatially variable soils via convolutional neural network

Chongzhi Wu et al.

Summary: This study proposes a convolutional neural network (CNN) surrogate model based on the classical architecture VGG6 for random field finite element analysis (RF-FEM). The model effectively reduces the computational burden and shows great potential in reliability analysis of spatially variable soils.

GONDWANA RESEARCH (2023)

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Article Construction & Building Technology

Investigation of geopolymer-based ultra-high performance concrete slabs against contact explosions

Jian Liu et al.

Summary: This study investigated local damage of fibre reinforced G-UHPC slabs subjected to contact explosions, revealing performance differences among different materials. Through numerical simulations and machine learning methods, empirical equations were derived for assessing local damage levels of slabs under contact explosions.

CONSTRUCTION AND BUILDING MATERIALS (2022)

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Article Environmental Sciences

Stress-strain behavior of modified expansive clay soil: experimental measurements and prediction models

Anoosheh Iravanian et al.

Summary: This study evaluates the potential of using sodium hydroxide (NaOH) as an additive to improve the stability of clay. The results indicate that NaOH can enhance the engineering properties of the soil. Furthermore, a comparative study between different mathematical models shows that the quadratic model (QM) performs well in predicting the stress-strain behavior.

ENVIRONMENTAL EARTH SCIENCES (2022)

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Article Environmental Sciences

An Investigation on the Behaviour of Geosynthetic Reinforced Quarry Waste Bases (QWB) Under Vertical loading

Ishfaq Rashid Sheikh et al.

Summary: The strength and rigidity of the base course are crucial for pavement performance. The use of geosynthetic materials to reinforce quarry waste can improve its load-bearing capacity. Artificial neural network analysis can predict deformation on the pavement.

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2022)

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Article Engineering, Geological

Thermal and mechanical characteristics of recycled concrete aggregates mixed with plastic wastes: experimental investigation and mathematical modeling

Behnam Ghorbani et al.

Summary: This study aims to evaluate the effect of waste PET on the properties of recycled concrete aggregate (RCA) as a pavement material, developing a resilient modulus constitutive model using the multivariate adaptive regression spline approach. The research provides insights into the thermal and mechanical properties of recycled materials for pavement and geotechnical applications.

ACTA GEOTECHNICA (2022)

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Article Polymer Science

Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques

Afnan Nafees et al.

Summary: This study focuses on developing modeling techniques to predict the compressive strength of silica fume concrete. Decision tree and support vector machine algorithms were used, and a reliable database of compressive strengths was established. The evaluation of models showed that decision tree models outperformed support vector machine models in predicting compressive strength. Sensitivity analysis revealed that cement and water were the most significant factors affecting compressive strength.

POLYMERS (2022)

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Review Chemistry, Physical

A review on the Choice of Nano-Silica as Soil Stabilizer

G. Kannan et al.

Summary: Soil stabilization using nano-additives, particularly nano-silica, shows promising results in improving soil strength and reducing hydraulic conductivity. However, challenges such as availability, cost, implementation difficulty, and practical feasibility still need to be addressed for wider adoption of this technology in geotechnical engineering field.

SILICON (2022)

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Article Engineering, Civil

Theoretical models to evaluate the effect of SiO2 and CaO contents on the long-term compressive strength of cement mortar modified with cement kiln dust (CKD)

Aso A. Abdalla et al.

Summary: Cement kiln dust (CKD), a waste material generated during cement production, can be used as a substitute for Portland cement in construction. This study evaluated the effect of CKD's components on the long-term compressive strength and developed models to predict it. The results showed that increasing SiO2 content increased the strength, while the effect of CaO depended on CKD content.

ARCHIVES OF CIVIL AND MECHANICAL ENGINEERING (2022)

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Article Computer Science, Interdisciplinary Applications

Surrogate Models to Predict the Long-Term Compressive Strength of Cement-Based Mortar Modified with Fly Ash

Aso Abdalla et al.

Summary: Supplementary cementing ingredients have become essential in concrete and cement-mortar mix design. This study used Multi Expression Programming (MEP) to estimate the compressive strength of cement mortar modified with fly ash. The MEP model outperformed other models according to evaluation tools.

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING (2022)

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Article Construction & Building Technology

Electrical resistivity-Compressive strength predictions for normal strength concrete with waste steel slag as a coarse aggregate replacement using various analytical models

Nzar Shakr Piro et al.

Summary: Steel slag can be used as a replacement material in concrete to improve compressive strength and reduce electrical resistivity. Different models have been employed to predict concrete performance, and the most accurate prediction was achieved using an Artificial Neural Network (ANN) model for compressive strength and a Full Quadratic model for electrical resistivity.

CONSTRUCTION AND BUILDING MATERIALS (2022)

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Article Environmental Sciences

Electrical conductivity, microstructures, chemical compositions, and systematic multivariable models to evaluate the effect of waste slag smelting (pyrometallurgical) on the compressive strength of concrete

Nzar Shakr Piro et al.

Summary: This study analyzed and modeled the effect of steel slag aggregate substitution in concrete by collecting a large amount of data and using different models for prediction. The study found that factors such as water/cement ratio, steel slag content, fine aggregate content, and coarse aggregate content have an impact on the compressive strength of concrete. In addition, steel slag also affects the electrical conductivity of concrete, and there is a correlation between compressive strength and electrical conductivity.

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2022)

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Article Chemistry, Physical

Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar

Hemn Unis Ahmed et al.

Summary: In this paper, multiscale models were developed to predict the compressive strength of fly-ash-based geopolymer mortar. Three different models, linear regression, multinominal logistic regression, and nonlinear regression, were compared and evaluated. The results showed that the nonlinear regression model performed the best. Additionally, the SiO2 percentage of fly ash and the alkaline liquid-to-binder ratio were identified as the most effective parameters for predicting the compressive strength of geopolymer mortar.

MATERIALS (2022)

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Article Engineering, Civil

Design and Analysis of Lightweight Alkali-Activated Slag and Fly Ash Geopolymer Mortars using ANFIS-SSO

T. Bhavani Chowdary et al.

Summary: In recent years, geopolymers have been seen as a potential replacement for cement in construction due to their advantageous characteristics. This study focuses on designing lightweight geopolymer mortar using fly ash and alkali-activated slag as base materials, and using Artificial Intelligence techniques to compute compressive strength. Performance evaluation is conducted by varying temperature and binder content.

IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING (2022)

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Article Engineering, Geological

Auto machine learning-based modelling and prediction of excavation-induced tunnel displacement

Dongmei Zhang et al.

Summary: In this study, an AutoML-based approach is proposed to predict excavation-induced tunnel displacements, with important variables being the ratio of shear strength to vertical effective stress E-ur/sigma(v)', excavation depth H, and excavation width B.

JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING (2022)

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Article Construction & Building Technology

A novel Bayesian inference method for predicting optimum strength gain in sustainable geomaterials for greener construction

S. C. Jong et al.

Summary: A machine learning technique based on Bayesian inference was proposed in this study to predict the strength gain in sustainable geomaterials. The results of the case studies showed that this approach could reliably and accurately predict the strength of geomaterials with different mix design requirements.

CONSTRUCTION AND BUILDING MATERIALS (2022)

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Article Construction & Building Technology

A Data-Driven Influential Factor Analysis Method for Fly Ash-Based Geopolymer Using Optimized Machine-Learning Algorithms

Guowei Ma et al.

Summary: This study developed an ensemble machine learning modeling method to predict the strength of fly ash-based geopolymer, showing that the XGBoost model outperformed others and accurately predicted the strength. The impact of curing conditions, alkali-activator solution variables, and the mole of sodium hydroxide on the model output was analyzed using the SHapley Additive exPlanations theory.

JOURNAL OF MATERIALS IN CIVIL ENGINEERING (2022)

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Article Chemistry, Physical

Strength Predictive Modelling of Soils Treated with Calcium-Based Additives Blended with Eco-Friendly Pozzolans-A Machine Learning Approach

Eyo U. Eyo et al.

Summary: This study applies the machine learning technique of gradient boosting to model the unconfined compressive strength of soils stabilized by cementitious additive-enriched agro-based pozzolans. The results show that the well-tuned and optimized gradient boosting algorithm can accurately distinguish between different strength categories and achieve low misclassification rates. The model also performs well in regression problems, demonstrating the high performance of the gradient boosting algorithm used.

MATERIALS (2022)

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Article

Prediction of Probability of Liquefaction Using Soft Computing Techniques

Divesh Ranjan Kumar et al.

Journal of The Institution of Engineers (India): Series A (2022)

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Article Construction & Building Technology

Combined Utilization of Construction and Demolition Waste and Propylene Fiber in Cement-Stabilized Soil

Genbao Zhang et al.

Summary: Construction and demolition waste can be effectively utilized as aggregates in polypropylene-fiber-reinforced cement-stabilized soil, resulting in improved mechanical properties. Optimal proportioning and the mechanism of strength enhancement were determined through experimental tests and scanning electron microscope (SEM) analysis. This research is significant for the effective utilization of waste and environmental sustainability.

BUILDINGS (2022)

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Article Engineering, Civil

An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures

Ehsan Momeni et al.

Summary: This study investigates the feasibility of two soft computing techniques in predicting the deformation of Geosynthetic reinforced soil (GRS) structures, showing that the Gravitational Search Algorithm (GSA)-based artificial neural network (ANN) model outperforms the Particle Swarm Optimization (PSO)-based ANN model. The findings suggest that the proposed models can be implemented in assessing the performance of geosynthetic reinforced soil structures.

TRANSPORTATION GEOTECHNICS (2021)

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Article Engineering, Geological

Multivariate adaptive regression splines model for reinforced soil foundations

M. N. A. Raja et al.

Summary: This study developed a MARS model to predict settlement of shallow reinforced sandy soil foundations, and compared its performance with four other machine learning regression models. The model was validated using data from previous scientific studies and demonstrated superior accuracy in predicting settlement of reinforced foundations.

GEOSYNTHETICS INTERNATIONAL (2021)

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Article Construction & Building Technology

Synthesis of rubberized Alkali-activated Concrete: Experimental and numerical evaluation

Mohammed Yahya Mohammed Al-Fasih et al.

Summary: This study evaluated the feasibility of using waste rubber tire crumbs as a partial replacement for natural aggregates in alkali-activated rubberized concrete production, showing that 30% replacement can improve the mechanical strength and ductility of concrete. The neural network technique was used for numerical model calculations to assess the overall performance of the rubberized concretes.

CONSTRUCTION AND BUILDING MATERIALS (2021)

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Review Construction & Building Technology

Geopolymer concrete as sustainable material: A state of the art review

Furqan Farooq et al.

Summary: The rise in population and improvement in lifestyle have led to a rapid increase in energy demand for buildings, prompting the development of sustainable infrastructure due to the lack of fossil fuels and environmental issues. Geopolymer composites are becoming popular for their sustainability and reduced greenhouse gas emissions compared to ordinary Portland cement.

CONSTRUCTION AND BUILDING MATERIALS (2021)

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Review Chemistry, Multidisciplinary

Comparison of Nanomaterials with Other Unconventional Materials Used as Additives for Soil Improvement in the Context of Sustainable Development: A Review

Gang Liu et al.

Summary: This paper compares nanomaterials with other three representative unconventional materials for soil treatment, demonstrating the superiority of nanomaterials. Nanomaterial and MICP have comprehensive effects on soil reinforcement, while recycled tire and environmental fibers are mainly used for soil reinforcement. The mechanisms and effects of these materials are discussed in detail, showing that nanomaterials have promising prospects in the market application.

NANOMATERIALS (2021)

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Review Computer Science, Artificial Intelligence

Application of deep learning algorithms in geotechnical engineering: a short critical review

Wengang Zhang et al.

Summary: With the rise of big data era, deep learning has become crucial in the field of artificial intelligence, attracting researchers globally. This study presents the state of practice of deep learning in geotechnical engineering, elaborating on four major algorithms and their applications.

ARTIFICIAL INTELLIGENCE REVIEW (2021)

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Article Engineering, Multidisciplinary

Experimental and numerical model for mechanical properties of concrete containing fly ash: Systematic review

Nazanin Fasihihour et al.

MEASUREMENT (2021)

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Review Engineering, Environmental

Use of steel slag as sustainable construction materials: A review of accelerated carbonation treatment

Qifeng Song et al.

Summary: Steel slag is a by-product of the steelmaking process characterized by abundant free calcium/magnesium oxide and high heavy metal content. Recent research has shown that accelerated carbonation can improve the properties of steel slag. The challenge lies in understanding the interconnectedness between influencing factors and their contributions to the improvement of steel slag properties.

RESOURCES CONSERVATION AND RECYCLING (2021)

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Article Green & Sustainable Science & Technology

Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete

Muhammad Izhar Shah et al.

Summary: In this study, sugarcane bagasse ash was used to produce green concrete, and predictive models were developed using MEP to simulate the mechanical properties of SCBA substitute concrete, showing a high level of accuracy.

SUSTAINABILITY (2021)

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Article Chemistry, Physical

Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP

Afnan Nafees et al.

Summary: This study aims to create modeling tools for estimating the compressive and cracking tensile strengths of silica fume concrete. Using a broad and accurate database, machine learning models achieve high prediction accuracy.

MATERIALS (2021)

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Article Chemistry, Multidisciplinary

Evaluating Biosedimentation for Strength Improvement in Acidic Soil

Ahmed Hassan Saad et al.

Summary: Marine clay soils lose structure and stability when subjected to construction loads. Soil improvement methods involving physical, biological, and chemical treatments can enhance stability and reduce deformations.

APPLIED SCIENCES-BASEL (2021)

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Article Crystallography

A State-of-the-Art Review on Suitability of Granite Dust as a Sustainable Additive for Geotechnical Applications

Gudla Amulya et al.

Summary: The increase in soil utilization drives the need for soil improvement, with granite dust emerging as a potential solution due to its industrial by-product nature. Research shows varying effects of granite dust in different applications, and its dosage plays a crucial role in various field scenarios.

CRYSTALS (2021)

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Article Engineering, Geological

Predicting the settlement of geosynthetic-reinforced soil foundations using evolutionary artificial intelligence technique

Muhammad Nouman Amjad Raja et al.

Summary: This study proposes a new hybrid technique, the ANN-GWO model, for predicting settlement of geosynthetic-reinforced soil foundations. The model's predictive power was evaluated through numerical simulations, statistical indices, and sensitivity analysis. Results indicate that the model can reliably and intelligently estimate maximum settlement of GRSF.

GEOTEXTILES AND GEOMEMBRANES (2021)

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Review Engineering, Civil

PEAT SOIL IMPROVEMENT WITH BAMBOO REINFORCEMENT TECHNOLOGY: A REVIEW

Faizah Md Talib et al.

Summary: Construction on soft soil, especially on peat soil, poses challenges due to its compressibility, low shear strength, and bearing capacity. Soil reinforcement techniques, such as bamboo technology, can improve the strength and stiffness of peat soil, reducing deformation and settlement impacts.

INTERNATIONAL JOURNAL OF GEOMATE (2021)

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Article Engineering, Multidisciplinary

Prediction of recycled coarse aggregate concrete mechanical properties using multiple linear regression and artificial neural network

Suhas Vijay Patil et al.

Summary: The study focuses on predicting the mechanical properties of concrete made from recycled coarse aggregate at 28 days curing using multiple linear regressions (MLR) and artificial neural network (ANN). The ANN model showed greater accuracy with R-2 value over 0.8 in training phase and 0.9 in testing phase, indicating a strong relation between actual and predicted values of mechanical properties.

JOURNAL OF ENGINEERING DESIGN AND TECHNOLOGY (2021)

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Article Materials Science, Multidisciplinary

Particles size and distribution on the improvement of the mechanical performance of high strength solid solution based inorganic polymer composites: A microstructural approach

Achile Nana et al.

Summary: The research explores the influence of particle size and distribution on the physical, mechanical, and microstructural features of inorganic polymer composites, finding that a mix of different particle sizes can achieve optimal mechanical performance with denser and tougher microstructure. Additionally, designing composites based on feldspathic rock with high strengths is seen as sustainable, low energy consumption, and environmentally-friendly for structural construction.

MATERIALS CHEMISTRY AND PHYSICS (2021)

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Article Engineering, Civil

Evaluation and prediction of slope stability using machine learning approaches

Shan Lin et al.

Summary: In this study, a machine learning model is built for slope stability evaluation, exploring various ML methods for factor of safety prediction. Through data preprocessing and model evaluation, SVM, GBR, and Bagging are identified as the best regression methods based on evaluation indicators R-2, MAE, and MSE.

FRONTIERS OF STRUCTURAL AND CIVIL ENGINEERING (2021)

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Proceedings Paper Materials Science, Multidisciplinary

Experimental study and neural network modelling of expansive sub grade stabilized with industrial waste by-products and geogrid

C. Rajakumar et al.

Summary: The study aimed to predict the soaked CBR value of stabilized soils using Multiple Regression Analysis (MRA) and Artificial Neural Network (ANN) modeling. Experimental results showed that the ANN model was more accurate than the MRA model, with a maximum R value of 0.94317 and minimum MSE value of 0.49.

MATERIALS TODAY-PROCEEDINGS (2021)

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Article Engineering, Civil

A Laboratory Investigation on a Mechanical Behavior of Sandy and Clayey Soils with Kraft Paper Fiber

Paulo Jose Rocha de Albuquerque et al.

Summary: The study focuses on using natural multifoliated kraft paper (NKP) fibers as reinforcement for sandy and clayey soils, aiming to improve engineering properties by increasing friction angle and shear strength. Results showed that the addition of NKP fibers enhanced soil ductility and maintained shear strength at high levels of strain.

TRANSPORTATION INFRASTRUCTURE GEOTECHNOLOGY (2021)

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Article Engineering, Civil

Experimental and computational response of strip footing resting on prestressed geotextile-reinforced industrial waste

Sufyan Ghani et al.

Summary: The study investigated the strength and deformation characteristics of ferrochrome slag reinforced with single prestressed geotextile layer through model load tests. It proposed sustainable solutions for industrial waste and found that prestressing of reinforcements is more effective than simple reinforcement in reducing settlement. Artificial neural network and ELM were suggested for settlement prediction, with computational models proving efficient and effective for preliminary assessments.

INNOVATIVE INFRASTRUCTURE SOLUTIONS (2021)

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Review Nanoscience & Nanotechnology

Durability of geopolymers and geopolymer concretes: A review

Tian Lingyu et al.

Summary: Geopolymers are green materials with excellent mechanical properties and fire resistance, but poor frost resistance. They also exhibit good corrosion resistance and permeability.

REVIEWS ON ADVANCED MATERIALS SCIENCE (2021)

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Article Engineering, Geological

Prediction of Enhanced Soil-Anchored Geogrid Interactions in Direct Shear Mode Using Gene Expression Programming

Mahmood Reza Abdi et al.

Summary: A model for predicting the Enhanced Interaction Coefficient (EIC) of soil-geosynthetic interactions was proposed using large-scale direct shear tests on soil-anchored geogrid samples; Artificial Intelligence method Gene Expression Programming (GEP) was adopted to develop the model and sensitivity analysis was performed on parameter importance; Excellent correlation between the GEP-based model and experimental results demonstrated the capability of effectively estimating soil-anchored geogrid enhanced interaction coefficient.

GEOTECHNICAL AND GEOLOGICAL ENGINEERING (2021)

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Review Computer Science, Artificial Intelligence

A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications

Hossein Moayedi et al.

NEURAL COMPUTING & APPLICATIONS (2020)

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Article Engineering, Civil

A Novel Formulation for the Compressive Strength of IBP-Based Geopolymer Stabilized Clayey Soils Using ANN and GMDH-NN Approaches

Danial Rezazadeh Eidgahee et al.

IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING (2020)

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Article Construction & Building Technology

Compaction performance analysis of alum sludge waste modified soil

Syyed Adnan Raheel Shah et al.

CONSTRUCTION AND BUILDING MATERIALS (2020)

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Article Computer Science, Artificial Intelligence

Hybrid machine learning for predicting strength of sustainable concrete

Anh-Duc Pham et al.

SOFT COMPUTING (2020)

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Review Construction & Building Technology

A review on self-compacting concrete incorporating palm oil fuel ash as a cement replacement

Kasali Adebayo Mujedu et al.

CONSTRUCTION AND BUILDING MATERIALS (2020)

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Review Green & Sustainable Science & Technology

Characteristics of biodegradable textiles used in environmental engineering: A comprehensive review

Daria Marczak et al.

JOURNAL OF CLEANER PRODUCTION (2020)

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Article Engineering, Civil

CASE STUDY ON THE EFFECT OF RECYCLED ASPHALT LAYER PARAMETERS ON THE BEARING CAPACITY OF THE PAVEMENT

Atis Zarins

BALTIC JOURNAL OF ROAD AND BRIDGE ENGINEERING (2020)

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Article Green & Sustainable Science & Technology

A Study on Mechanical Properties of Concrete Incorporating Aluminum Dross, Fly Ash, and Quarry Dust

Mohamed Hamdy Elseknidy et al.

SUSTAINABILITY (2020)

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Review Engineering, Geological

Rainfall-Induced Slope Failures and Use of Bamboo as a Remedial Measure: A Review

J. S. Gidon et al.

INDIAN GEOTECHNICAL JOURNAL (2020)

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Article Multidisciplinary Sciences

Ternary Diagrams for Predicting Strength of Soil Ameliorated with Different Types of Fly Ash

Adil Binal et al.

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING (2020)

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Article Computer Science, Interdisciplinary Applications

Evaluating unconfined compressive strength of cohesive soils stabilized with geopolymer: a computational intelligence approach

Hamed Javdanian et al.

ENGINEERING WITH COMPUTERS (2019)

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Article Construction & Building Technology

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

Benjamin A. Young et al.

CEMENT AND CONCRETE RESEARCH (2019)

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Article Engineering, Geological

Prediction of footing settlements with geogrid reinforcement and eccentricity

K. Dal et al.

GEOSYNTHETICS INTERNATIONAL (2019)

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Article Green & Sustainable Science & Technology

Assessing the life cycle CO2 emissions of reinforced concrete structures: Four cases from China

Hui Li et al.

JOURNAL OF CLEANER PRODUCTION (2019)

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Article Chemistry, Physical

Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete

Dong Van Dao et al.

MATERIALS (2019)

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Article Chemistry, Multidisciplinary

Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches

Dong Van Dao et al.

APPLIED SCIENCES-BASEL (2019)

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Article Engineering, Chemical

Performance Evaluation of Sustainable Soil Stabilization Process Using Waste Materials

Muhammad Aamir et al.

PROCESSES (2019)

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Review Green & Sustainable Science & Technology

Applications of rice husk ash as green and sustainable biomass

Hossein Moayedi et al.

JOURNAL OF CLEANER PRODUCTION (2019)

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Article Engineering, Geological

Reliability Analysis of Pile Foundation Using ELM and MARS

Manish Kumar et al.

GEOTECHNICAL AND GEOLOGICAL ENGINEERING (2019)

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Article Computer Science, Interdisciplinary Applications

Predicting compressive strength of lightweight foamed concrete using extreme learning machine model

Zaher Mundher Yaseen et al.

ADVANCES IN ENGINEERING SOFTWARE (2018)

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Review Computer Science, Interdisciplinary Applications

A review of the automated detection and classification of acute leukaemia: Coherent taxonomy, datasets, validation and performance measurements, motivation, open challenges and recommendations

M. A. Alsalem et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2018)

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Article Construction & Building Technology

Effect of calcined Czech claystone on the properties of high performance concrete: Microstructure, strength and durability

Eva Vejmelkova et al.

CONSTRUCTION AND BUILDING MATERIALS (2018)

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Article Engineering, Geological

Shear strength of interfaces between unsaturated soils and composite geotextile with polyester yarn reinforcement

Apiniti Jotisankasa et al.

GEOTEXTILES AND GEOMEMBRANES (2018)

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Review Engineering, Civil

Utilization of Recycled Tiles and Tyres in Stabilization of Soils and Production of Construction Materials - A State-of-the-Art Review

Mohammed Ali Mohammed Al-Bared et al.

KSCE JOURNAL OF CIVIL ENGINEERING (2018)

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Review Engineering, Environmental

Characteristics of steel slags and their use in cement and concrete-A review

Yi Jiang et al.

RESOURCES CONSERVATION AND RECYCLING (2018)

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Article Construction & Building Technology

Artificial neural network based modelling approach for strength prediction of concrete incorporating agricultural and construction wastes

Mulusew Aderaw Getahun et al.

CONSTRUCTION AND BUILDING MATERIALS (2018)

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Article Engineering, Geological

Predicting the Settlement of Raft Resting on Sand Reinforced with Planar and Geocell Using Generalized Regression Neural Networks (GRNN) and Back Propagated Neural Networks (BPNN)

Vikas Kumar et al.

INTERNATIONAL JOURNAL OF GEOSYNTHETICS AND GROUND ENGINEERING (2018)

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Review Green & Sustainable Science & Technology

A huge number of artificial waste material can be supplementary cementitious material (SCM) for concrete production - a review part II

Evi S. Aprianti

JOURNAL OF CLEANER PRODUCTION (2017)

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Article Engineering, Civil

Energy-Based Estimation of Soil Liquefaction Potential Using GMDH Algorithm

Hamed Javdanian et al.

IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING (2017)

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Proceedings Paper Construction & Building Technology

Utilization of fly ash as partial sand replacement in oil palm shell lightweight aggregate concrete

A. Z. Muhammad Nazrin Akmal et al.

GLOBAL CONGRESS ON CONSTRUCTION, MATERIAL AND STRUCTURAL ENGINEERING 2017 (GCOMSE2017) (2017)

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Article Engineering, Geological

Hydraulic Conductivity of Compacted Granite Residual Soil Mixed with Palm Oil Fuel Ash in Landfill Application

Nik Norsyahariati Nik Daud et al.

GEOTECHNICAL AND GEOLOGICAL ENGINEERING (2017)

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Review Computer Science, Artificial Intelligence

A systematic review and meta-Analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments

Abbas Mardani et al.

APPLIED SOFT COMPUTING (2017)

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Article Construction & Building Technology

Influence of aging conditions upon the properties of calcined clay and its performance as supplementary cementitious material

Ricarda Gmuer et al.

CEMENT & CONCRETE COMPOSITES (2016)

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Article Materials Science, Ceramics

Development of fly ash and iron ore tailing based porous geopolymer for removal of Cu(II) from wastewater

Ping Duan et al.

CERAMICS INTERNATIONAL (2016)

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Article Construction & Building Technology

Characterization of metakaolin and study on early age mechanical strength of hybrid cementitious composites

Mahyuddin B. Ramli et al.

CONSTRUCTION AND BUILDING MATERIALS (2016)

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Article Construction & Building Technology

Investigation of coal bottom ash and fly ash in concrete as replacement for sand and cement

Mandi Rafieizonooz et al.

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