Civil Engineering: Recent submissions

Now showing items 1-20 of 318

Investigation of the compactability of pervious concrete and its impact on porosity and compressive strength

Subramaniam, D.N. (Taylor & Francis, 2025)

Compressive strength is often predicted from porosity or mix-design parameters. The inability to convert laboratory scale-compaction-energy to industrial-scale limits the application of performance prediction models. ...
Investigation of Compaction on Compressive Strength and Porosity of Pervious Concrete

Sajeevan, M.; Subramaniam, D.N.; Rinduja, R.; Pratheeba, J. (Chinese Society of Pavement Engineering, 2025)

Pervious concrete (PC) is a sustainable substitute for conventional concrete application yet limited due to lack of understanding on its performance characteristics. The mix design affects the performance, mainly due to ...
Investigation on the effectiveness of fourier shape analysis in classifying milled aggregates

Mithulavan, V.; Samarasinghe, T.; Valluvan, R.; Karnan, N.; Sathiparan, N.; Subramaniam, D.N. (Elsevier, 2025)

Material that constitute aggregate depend on their packing efficiency that is defined by the shape distribution of aggregates. Geometrical shape descriptors would not capture morphological aspects of different dimensional scales ...
Investigation of impact of aggregate shape on pervious concrete using machine learning classification methods

Wijekoon, S.H.B.; Ahilash, N.; Pravinjan, V.; Virupashan, K.; Sathiparan, N.; Jeyananthan, P.; Subramaniam, D.N. (Elsevier, 2025)

Optimizing compaction energy reduces uncertainty in mass production of pervious concrete, but depends on aggregate-shape. This study analyses impact of aggregate size and shape on porosity and compressive strength. Agg ...
Predicting compressive strength in cement mortar: The impact of fly ash composition through machine learning

Sathiparan, N. (Elsevier, 2025)

This study investigates the significant impact of fly ash’s chemical composition on cement mortar’s compressive strength, employing advanced machine-learning models to enhance prediction accuracy. As the demand for ...
The impact of oxides of cementitious materials on mortar strength: A machine learning perspective

Sathiparan, N. (Elsevier, 2025)

This study uses machine learning to predict the compressive strength of cement-sand mortar incorporating supplementary cementitious materials (SCMs). The research addresses a gap in the literature by specifically examining ...
A systematic review of corncob ash in construction: Current findings and future directions

Sathiparan, N. (Elsevier, 2025)

The increasing environmental impact of traditional cement production necessitates the exploration of sustainable alternatives in construction materials. This paper investigates corncob ash (CCA), an agro-waste by-product, ...
Soft computing to predict the porosity and permeability of pervious concrete based on mix design and ultrasonic pulse velocity

Sathiparan, N.; Wijekoon, S.H.; Jeyananthan, P.; Subramaniam, D.N. (Taylor & Francis, 2024)

The present study explores the potential of machine learning to predict the porosity and permeability of pervious concrete constructed on mix parameters (compaction energy, aggregate-to-cement ratio and aggregate size) ...
Response surface regression and machine learning models to predict the porosity and compressive strength of pervious concrete based on mix design parameters

Sathiparan, N.; Wijekoon, S.H.; Ravi, R.; Jeyananthan, P.; Subramaniam, D.N. (Taylor & Francis, 2025)

This study investigates the influence of aggregate size, aggregate-to-cement ratio, and compaction effort on pervious concrete's porosity and compressive strength. It proposes using response surface methodology and machine ...
Quantifying the impact of chemical composition on pervious concrete strength: a comparative analysis using full quadratic model and artificial neural network

Sathiparan, N.; Jeyananthan, P.; Subramaniam, D.N. (Taylor & Francis, 2024)

The present study investigates the effect of the total amount of chemical constituents in cement and supplementary cementitious materials on the compressive strength of pervious concrete. Experimental datasets were collected ...
Predicting compressive strength of quarry waste-based geopolymer mortar using machine learning algorithms incorporating mix design and ultrasonic pulse velocity

Sathiparan, N.; Jeyananthan, P. (Taylor & Francis, 2024)

The current study aimed to investigate the possibility of predicting the compressive strength of geopolymer mortar by mix design parameters, ultrasonic pulse velocity (UPV) and machine learning techniques. Here the geopolymer ...
Optimisation of pervious concrete performance by varying aggregate shape, size, aggregate-tocement ratio, and compaction effort by using the Taguchi method

Wijekoon, S.H.B.; Sathiparan, N.; Subramaniam, D.N. (Taylor & Francis, 2024)

The porosity and compressive strength of pervious concrete are critical determinants of its suitability for various applications. Therefore, it is necessary to recognise the factors influencing the performance and its ...
Comparative study of fly ash and rice husk ash as cement replacement in pervious concrete: mechanical characteristics and sustainability analysis

Subramaniam, D.N.; Sathiparan, N. (Taylor & Francis, 2023)

Pervious concrete is a special type of concrete consisting of cement, coarse aggregate and water. Cement is a widely used raw material for construction including pervious concrete and had led to the release of huge amounts ...
Characterisation of the shape of aggregates using image analysis

Subramaniam, D.N.; Dassanayake, D.H.H.P.; Ahilash, N.; Wijekoon, S.H.B.; Sathiparan, N. (Taylor & Francis, 2024)

Aggregates are packed effectively packed in applications including pervious concrete, mechanical performance of which, depends primarily on packing characteristics. Size and shape distribution of aggregates affect packing. ...
Silica fume as a supplementary cementitious material in pervious concrete: prediction of compressive strength through a machine learning approach

Sathiparan, N.; Jeyananthan, P.; Subramaniam, D.N. (Springer, 2024)

Utilizing silica fume as a substitute for cement in pervious concrete offers a viable approach to achieve sustainability within the realm of construction industry. The mechanical characteristics of pervious concrete are ...
Statistical Assessment of Different Aggregate Shape Factors

Wijekoon, S.H.B.; Subramaniam, D.N.; Sathiparan, N. (Springer, 2023)

This study aims to investigate aggregate geometric characteristics and analyze the possible aggregate shape factors on their statistical relationships. The study used 2192 crushed rock particles from 5 to 30 mm in diameter ...
Statistical investigation of aggregate size and shape impact on porosity and compressive strength of pervious concrete

Sajeevan, M.; Ahilash, N.; Shobijan, J.; Pravinjan, V.; Virupashan, K.; Sathiparan, N.; Subramaniam, D.N. (Taylor & Francis, 2025)

The shape and size of aggregates are rarely investigated in pervious concrete research despite their impact on porosity and compressive strength. This study analyses the impact of size and shape distribution of aggregates and ...
Prediction of moisture content of cementstabilized earth blocks using soil characteristics, cement content, and ultrasonic pulse velocity

Sathiparan, N.; Tharuka, R.A.N.S.; Jeyananthan, P. (Springer Nature, 2024)

This article investigates the importance of moisture content in cement-stabilized earth blocks (CSEBs) and explores methods for their prediction using machine learning. A key aspect of the research is the development of ...
Soft computing techniques to predict the compressive strength of groundnut shell ash‑blended concrete

Sathiparan, N.; Jeyananthan, P. (Springer Nature, 2023)

Using groundnut shell ash (GSA) as a component in concrete mixtures is a viable approach to achieving sustainability in building practices. This particular kind of concrete has the potential to effectively mitigate the ...
Comparative analysis of machine learning models and standard codes for predicting compressive strength in hollow block masonry

Sathiparan, N.; Jeyananthan, P. (Springer, 2025)

This study employs machine learning models to predict the compressive strength of hollow block masonry and examines the key factors that influence this strength. Accurate prediction of its compressive strength is crucial ...

Now showing items 1-20 of 318

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