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Research Article
Modified Pervious Concrete Pavement with Lime Mortar and Recycled Plastic Fibers for Urban Infrastructure in Bangladesh
Issue:
Volume 13, Issue 4, August 2025
Pages:
185-196
Received:
8 June 2025
Accepted:
23 June 2025
Published:
16 July 2025
Abstract: This study evaluates the mechanical and permeability performance of a Modified Pervious Concrete Pavement (MPCP) developed for urban infrastructure in Bangladesh. The MPCP incorporates lime mortar, selected for its binding properties, and recycled plastic bottle fibers, introduced to enhance tensile strength, crack resistance, and durability. A series of mix designs were developed and tested to assess the effects of varying proportions of lime mortar and plastic fibers on the structural and hydraulic characteristics of the pavement. Among the tested configurations, the A5 mix (cement: lime mortar: aggregate = 1:0.25:3) demonstrated an effective balance between strength and porosity. It achieved a 28-day compressive strength of 18.445 MPa and a porosity of 17.01%, meeting functional criteria for pervious pavement applications. Additionally, the A5 mix exhibited a high infiltration rate of 483.362 mm/hour, supporting its suitability for stormwater management in flood-prone areas. The experimental findings indicate that the integration of lime mortar and recycled plastic fibers can improve both mechanical and permeability characteristics of pervious concrete without compromising its fundamental design properties. The use of locally sourced and waste-derived materials further supports resource-efficient construction practices. This study provides a framework for the development of structurally sound and hydraulically functional pervious pavement systems tailored to the environmental and infrastructural context of Bangladesh.
Abstract: This study evaluates the mechanical and permeability performance of a Modified Pervious Concrete Pavement (MPCP) developed for urban infrastructure in Bangladesh. The MPCP incorporates lime mortar, selected for its binding properties, and recycled plastic bottle fibers, introduced to enhance tensile strength, crack resistance, and durability. A ser...
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Research Article
Automated Multi-Class Concrete Crack Detection and Severity Classification Using CNN-Based Deep Learning
Wisam Bukaita
,
Kalyan Naik Vankudothu*,
Junaid Khan
Issue:
Volume 13, Issue 4, August 2025
Pages:
197-210
Received:
17 June 2025
Accepted:
1 July 2025
Published:
22 July 2025
Abstract: Structural integrity is essential to sustainable infrastructure development, particularly in concrete structures. These are prone to deterioration from environmental exposure, mechanical stress, and corrosion. Conventional inspection techniques such as manual surveys and non-destructive testing (NDT)—are labor-intensive, time-consuming, and often limited by human accuracy, making them unsuitable for large-scale deployment. This research proposes an automated system using a custom Convolutional Neural Network (CNN) architecture tailored for concrete defect detection and severity classification. The model was built with four convolutional blocks (32–256 filters), max-pooling layers, batch normalization, and a final dense layer, totaling approximately 129,000 parameters. It was trained on a custom-labeled dataset of 21,000 images (20,000 crack images and 1,000 corrosion images), collected from publicly available repositories and manually classified into seven categories: No Cracks, Hairline Cracks, Small Cracks, Moderate Cracks, Large Cracks, Very Large Cracks, and Cracks Due to Corrosion. Data augmentation techniques were used to address class imbalance and improve generalization. Experimental results showed 94.7% classification accuracy, 93.5% precision, 92.8% recall, and a 93.1% F1 score. The system processes ~25 images/sec on an NVIDIA RTX 3060 GPU, making it suitable for real-time applications. This system represents a scalable, high-performance approach to infrastructure health monitoring, contributing to safer and more effective structural maintenance.
Abstract: Structural integrity is essential to sustainable infrastructure development, particularly in concrete structures. These are prone to deterioration from environmental exposure, mechanical stress, and corrosion. Conventional inspection techniques such as manual surveys and non-destructive testing (NDT)—are labor-intensive, time-consuming, and often l...
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Research Article
Comprehensive Evaluation of PCA-based Engineering Sweet Spot Logging in Tight Sandstone Reservoirs -- Example of Y96 Well in Long 7 Section of Tiezhuzi Block in Ordos Basin
Song Wenying*,
Chen Junbin,
Gong Diguang,
Wang Xiaoming,
Shi Ruidong,
Zhang Chengming
Issue:
Volume 13, Issue 4, August 2025
Pages:
211-221
Received:
3 June 2025
Accepted:
8 July 2025
Published:
23 July 2025
Abstract: Under the geological conditions of sandstone reservoirs in the long 7 sections of Tiezhuizi block, with the increase in the depth of burial and the complexity of geological structure, it leads to the status quo of generally low production capacity of horizontal wells. In the face of this challenge, the optimisation of fracturing engineering desserts is particularly difficult. To cope with this challenge, this study is dedicated to finding a high-precision method for quantitative evaluation of reservoir engineering sweet spots. In this study, principal component analysis was adopted to comprehensively and meticulously analyse nine key engineering sweet spot factors, including core density, elastic modulus, Poisson's ratio, and perimeter pressure. The screening criteria of eigenvalue > 1 accurately identified 2 factors that mainly affect the engineering sweet spot. The cumulative explained variance of these two principal components reaches 91.199 %, which almost covers most of the information. By analysing the positive and negative correlations between the factor loading coefficients of these 2 principal components affecting the engineering sweet spot, these two principal components were identified as the damage resistance factor and the external confining stress factor, respectively. By analysing the rock number composite scores of the principal components, the specific locations of the dominant reservoirs were precisely located, and the dominant reservoirs were located at 2085-2095m, 2035-2045m, 1955-1965m, 1975-1985m and 2005-2015m. This result is more conducive to the realisation of the project, with high accuracy.
Abstract: Under the geological conditions of sandstone reservoirs in the long 7 sections of Tiezhuizi block, with the increase in the depth of burial and the complexity of geological structure, it leads to the status quo of generally low production capacity of horizontal wells. In the face of this challenge, the optimisation of fracturing engineering dessert...
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