Mehran University Research Journal of Engineering and Technology

Combine effect of fly ash and aggregate size on flexural strength of reinforced concrete

Dileep Kumar — 2025-01-01

The study emphasizes the importance of utilizing fly ash and optimized aggregate sizes to enhance structural performance while promoting sustainable construction practices by reducing cement usage. In this research study, the combined effect of fly ash and aggregate size on the flexural strength of reinforced concrete is investigated. In this experimental study, a total of 12 batches were prepared with varying fly ash proportions and coarse aggregate sizes. For all batches, cement was replaced with fly ash by 0%, 5%, 10%, and 15% by weight of cement, and three different sizes of coarse aggregates (6.25 mm, 12 mm, and 20 mm) were used. The mix design and water cement ratios were set to (1:2:4) and 0.48, respectively. Prism-type RCC beams of size 100mm x 100mm x 500mm were casted for testing to evaluate density, ultimate load, and ultimate strength. Results revealed that reinforced concrete batch B2, containing 5% fly ash and 12.5 mm aggregate size, achieved a higher density and sustained an ultimate load 56.48% higher than nominal concrete. Furthermore, deflection in reinforced concrete batch D1 containing 15% fly ash and 20-mm aggregate size decreased by 12.13% compared to nominal concrete. The results showed that the combined effect of fly ash and aggregate size will minimize deflection and will provide sufficient flexural strength to sustain structural load effectively.

A Scheme Based on Deep Learning for Fruit Classification

Ali Orangzeb Panhwar — 2025-01-02

Grading and classifying fruits are critical due to automated machine learning systems. In computer vision, different fruits have large complexity and similarity to identify the fruit types. In this study, we developed an efficient and reliable fruit grading system. It is very difficult to classify fruits from images with established conventional approaches. We used a Convolutional Neural Network (CNN) methodology involving comparing a custom-built CNN and the VGG pre-trained models. In the research results, the VGG model accuracy is of 99.98 percent. This research proved the effectiveness of the deep model in the challenges of fruit classification and set a foundation for its application in automated grading systems.

Design, analysis, and model fabrication of an industrial manipulator for quality control purposes using computer vision as a sorting mechanism

Muthair Saeed — 2025-01-02

The industrial manipulator uses complex control systems and requires specialist equipment for object detection and sorting. These are expensive and complicated. Installing and maintaining these manipulators require professional personnel and equipment. The manipulator design in this project is fully modular, easier to automate, and inexpensive, which is designed to enable industrial automation in developing countries. Using Topology in conjunction with Fusion 360 and ANSYS material consumption is reduced by up to 30% while keeping the material strength and stress constant. 3D printing parts help in reducing manufacturing complexity, time, and material consumption by reducing the overall manufacturing cost. With a Workspace of two feet and an operating temperature range from 10-60 °C, it is designed to deliver 3 DOF. To prevent the system from overheating, an effective cooling system for the controller and driver, is also developed using CFD analysis. The manipulator is linked with several sensors and a camera, to provide full autonomous control. As a result, it can be utilized in various industrial applications for precise and quick output. In this project, a manipulator is designed to sort objects as they come off the conveyor and pick and place them at the design location. A conveyor is intended for item movement, both manipulator and conveyor are controlled by a microcontroller and a PC via Computer vision. Initially sorting of objects is performed through both color sensors. Then, using computer vision, an algorithm is trained to identify any defective item among the bunch using a camera and place it at the designated location. This computer vision algorithm can detect an object at a range of 3 feet and provides proper control with an accuracy of 90-95%, depending on the amount of information about the object collected and analyzed. This algorithm is also adaptable, as it can identify any object once it has been trained.

An assessment of the effects of manual energy and material haulage on the initial embodied environmental impact of residential buildings

Emmanuel Udomiaye — 2025-01-02

The energy inputs associated with all the stages of production of a building are known as initial embodied energy. These stages range from the mining of natural materials to manufacturing, transportation, and construction. . Inadequate local data in the formulation of climate change mitigation strategies have made studies on the effects of construction and transportation energy/emissions very significant. The study aims to appraise the impact of material haulage and site construction processes on the initial embodied energy and emission in the Nigerian context with the view of identifying the effects of manual labour and material haulage. The objectives are: to estimate the transportation energy, and site construction, and identify the percentage of energy from manual work. The study adopted a case study methodology using multiple case studies and integrated the international energy/emission protocol. Construction energy and carbon emissions accounted for 3.9% and 1.52% respectively. Manual energy was found to be significant with an average manual energy intensity for the study area at 9.5MJ/m2. Also, transportation energy accounted for 11.65% of the initial embodied energy and 6.95% of emissions. Thus, recommended sustainable haulage approaches such as the reduction and enforcement of age restrictions on imported used trucks from <15 years to <5 years.

Weather identification using models based on deep learning

Afroza Nahar — 2025-01-02

Accurate weather forecasting is increasingly crucial as climate change intensifies the unpredictability of weather patterns, posing challenges to traditional forecasting models reliant on human observation or numerical methods. Researchers are working on precise weather forecasting to improve our preparedness, enabling fast response to any disaster. Among other techniques, deep learning is a prudent method to predict weather forecasts since it can automatically learn and train from a vast amount of data to generate and portray accurate features of an incident. This study evaluates deep learning techniques for weather forecasting based on different meteorological characteristics. This paper examines a few weather variables to evaluate the prediction performance of several deep learning solutions using TensorFlow and pre-trained Keras applications models. For this purpose, the top ten accuracy-based deep learning model architectures have been investigated and evaluated. The operation of each model is distinct. Models like EfficientNetB7, ResNet, MobileNet, VGG19, Xception Inception, ResNetV2, and VGG16 employ a combination of image classification and deep learning models to predict the weather. The WEAPD dataset of 6877 images representing 11 weather phenomena categories was utilized, and the models were trained and validated using an 80:10:10 split. Predictions, extraction of features, and fine-tuning of models were achieved with an accuracy of up to 83.39%. Most models performed well in image classification, enhancing the proposed framework and achieving significant precision in generating weather photos and reports.

Design and fabrication of an engineered multi-mode worker toolkit for construction and agriculture industries

Imran Khan — 2025-01-03

The workers at construction and agricultural sites face many problems regarding load carrying (body pains), which results in less work efficiency. Therefore, the idea of designing a toolkit is put forward, with the help of which the workers could do their work easily and efficiently. This study aims to design and fabricate a low-cost toolkit that is lighter and easily accessible to all workers. The toolkit is designed to be used in different positions or modes. The most important step in this project is material selection, as it will define the toolkit's final weight, cost, and strength. Various materials, such as wood, bamboo, aluminum, steel, etc., were compared based on their physical properties, such as their strength, weight, and behavior in changing weather. The material selected is steel due to its high strength, weldability, and low cost. A design was finalized by incorporating safety and worker comfort. An ANSYS analysis was then performed on the proposed design to check it for loading capacity and deformation in various modes of operation. After performing the ANSYS analysis, the toolkit was fabricated and tested at various modes of operation. It was found that the tool performed exceptionally well at loads less than 20 Kg, whereas there were some minute deflection errors (max. error of 1.7 mm) at loads greater than 35 Kg.

Comparison of yarn properties of gossypium hirsutum and naturally colored gossypium arboreum cotton

Syeda Hafsa Hassan — 2025-01-03

Naturally colored cotton can be a highly sustainable alternative to conventional cotton which entails huge consumption of water, energy, and chemicals in scouring, bleaching and dyeing. The objective of this study was to investigate the yarn properties of one of the most commonly grown Gossypium hirsutum cotton and naturally colored Gossypium arboreum cotton grown in Pakistan. Eighteen different samples of ring-spun yarn were developed using the two cotton types, in three different yarn counts (Ne 16, Ne 20, Ne 24), each with three different twist multipliers (TM: 4.00, 4.25, 4.50). Different yarn properties of all yarn samples were investigated as per standard test methods. Result comparisons show that tenacity and elongation of conventional cotton yarns was about 42% and 10 % higher as compared to that of colored cotton respectively. Similarly, hairiness, CVm and total imperfections of colored cotton yarns were 11%, 25%and 320 % higher as compared to conventional cotton respectively. Comparative analysis reveals that although yarns made from Gossypium hirsutum cotton are superior in terms of strength, elongation and uniformity. However, the properties of naturally colored Gossypium arboreum cotton yarn are good enough for making different textile products which do not require too high mechanical strength and uniformity such as knitwear, woven casuals, and home textiles like bed linen and curtains. It seems that the natural color genes of cotton suppress its strength and fiber length properties which reduced yarn tenacity, and increased yarn unevenness, imperfections, and hairiness.

Cyber Security Intrusion Detection Using a Deep Learning Method

Basheer Ullah — 2025-01-03

The World is moving towards information technology dependence, the cornerstone of which is information security. As the number of active connections becomes large so is the need of security increasing day by day. Presently, billions of devices are connected and every hour 0.46 Million new devices are connected to the web. Hence, due to this huge increase, the number of interconnections and the use of diverse protocols increases. Information and cyber security is a challenge worldwide and a big issue in business. One of the major aspects of information security is intrusion detection. It is important for cyber protection due an increasing number of cyber-attacks. Present methods to detect, predict and prevent malware still fall short of the desired level. The new techniques of deep learning are poised to succeed for detecting intrusion by employing different algorithms of detection and prevention. This paper proposes a deep neural network (DNN) for intrusion detection by the use of Kaggle NLS-KDD dataset with the highest attained accuracy of 92%. This detection method may prove to be very useful for ensuring cyber security of computers hence preventing data and economic loss.

Deep learning for multi-modal data fusion in IoT applications

Anila Saghir — 2025-01-03

With the rapid changes in technology, the Internet of Things (IoT) has also emerged with many diverse applications. A massive amount of data is generated and processed through the IoT-based sensors from these applications every day. This sensor-based data is categorized as either structured or unstructured data. Structured data is simpler to process, while the processing of unstructured data is complex, due to its diverse modalities. In IoT applications such as autonomous navigation, environmental monitoring and smart surveillance, semantic segmentation is required, and it relies on detailed scene understanding. The single-modal data like RGB, thermal or depth images fails to provide this detailed information independently. This research proposes a robust solution by fusing the multimodal data and employing a deep learning-based hybrid architecture that incorporates a generative model with a deep convolutional network. The unified model fuses RGB, thermal and depth images for semantic segmentation to improve the accuracy and reliability. The successful results validate the effectiveness of the proposed technique.

Investigating the performance parameters of solar cell for efficiency enhancement

Muhammad Muneeb Khan — 2025-01-03

Copper Zinc Tin Selenide (CuZnTSe) cells have unique physical characteristics that make them incredibly well-suited to serve as a solar material for power absorption in contemporary optimized Thin Film Solar cells (TFSC). The goal of the proposed study is to model novel hetero-structures for CuZnTSe solar cells with and without a NiO hole transport layer (HTL), namely AlZno2/Cds/CuZnTSe/Mb and Al-Zno2/Cds/CuZnTSe/Nio2/Mb. To develop and simulate the properties of the CuZnTSe cell and its possible applications in photovoltaic solar cells, simulation software SCAPS-1D is utilized. In addition to nickel oxide (NiO) serving as the electron-blocking HTL, transparent conductor oxide films such as aluminum zinc oxide (AlZnO) and cadmium sulfide (Cds) were added as electron transport layers. The incorporation of a NiO hole transport layer, carrier concentration, layer thickness, defect density, and other parameters impacting cell efficiency were all examined in this work. For the configurations under analysis, the maximum power efficiencies were 23.09% and 39.54%, respectively, with top fill-factors exceeding 79.47% and 87.21%.

Mathematical and finite element modelling of sustainable portable grain segregation system for the HDPE industry

Muhammad Huzaifa Ansari — 2025-01-02

High-density polyethylene (HDPE) pipe manufacturers have difficulties when contaminants are introduced in HDPE grains during recycling, resulting in irregular sizes and quality problems. Cleaning by hand takes a lot of time, especially when managing a 130 kg grain load every day. The current research focuses on designing a portable segregation system that replaces traditional techniques while requiring less Labor and a shorter manufacturing time that meets engineering standards with versatile industrial applications. The core objective is to develop a process for efficiently screening HDPE grain with different size meshes while taking care to handle the material with care to avoid any damage or deterioration by replacing traditional methods. The Grain Segregation System is used as a precise and space-efficient linear segregation technology to remove undesirable detritus affordably with three sieve plates ASTM E11 6.3 mm, 3.5 mm, and 2 mm pore sizes. The crank-slider mechanism is the working principle, powered by an AC motor. It works well for separating dry particles but has limitations for wet particles. Substituting sieve meshes, helps companies strive for effective grain segregation because of its easy operation, low maintenance requirements, and versatility. Furthermore, the mathematical modelling, structural, and Modal analysis of the design is investigated, to check the durability of the design under stresses using Finite Element Analysis (FEA) techniques. This research is not only sustainable in terms of safety, but in cost also as it reduces cost up to 65% depending upon the market value which ranges from 1000 – 4000 US Dollars.

Resilient routing in ad hoc wireless sensor networks

Tehmina Karamat Khan — 2025-01-02

Ad hoc Wireless Sensor Networks (WSNs) are specialized networks comprising a collection of sensor nodes that communicate to perform a specific task or record data in a decentralized manner. A considerable amount of work has been done regarding security, power saving, and other issues related to ad hoc wireless sensor networks, but there have been fewer efforts to improve performance, optimal utilization, and minimize packet loss. This paper has devised an algorithm for identifying reliable routes for efficient and intelligent packet transmission in wireless network topology. A decision-based tree mechanism for routing has been developed, capable of establishing routes in an ad-hoc sensor network that can be transformed into a logical dual ring. The system also proposes embedding resilience and reliability features from the Resilient Packet Ring (RPR) and Quality of Service (QoS) mechanisms. The proposed scheme is also applicable to Fog/Edge Networks (FENs). It aims to create an easily deployable and adaptable routing protocol that leverages all devices to support seamless mobile-to-mobile communications and wireless interfaces. Besides, it performs effectively across a wide range of connectivity scenarios in uncertain FEN environments.

Intelligent energy management in IoT-enabled smart homes: Anomaly detection and consumption prediction for energy-efficient usage

Laviza Falak Naz — 2025-01-02

The increasing Internet of Things (IoT) device integration in smart home environments has increased the options available for intelligent energy management. In the context of smart homes, this paper provides a detailed analysis on the use of IoT data for energy consumption trend prediction and anomaly detection. We propose a novel approach that combines the advantages of the Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models for accurate consumption forecasting. Real-world data from a smart home setting is utilised to evaluate the proposed models. Results will therefore show that our approach performs best in optimally utilizing resources, minimizing waste, and improving energy consumption. The current study contributes to the development of energy-efficient smart houses through providing a reliable method for consumption forecasting and anomaly detection. Results indicate that the LSTM model outperformed ARIMA in prediction accuracy, achieving a lower Mean Absolute Error (MAE) of 0.110 compared to ARIMA's 0.176. Furthermore, the LSTM model demonstrated superior performance in anomaly detection, with higher precision and recall scores.

Wheat genotype responses to phosphorus and zinc: Differential growth, mycorrhizal interactions, and zinc accumulation

Amanat Ali — 2025-01-02

Agronomic methods for increasing wheat's grain zinc (Zn) content have been found to be an effective means of preventing human zinc deficiency. To assess the response of three wheat genotypes to varying quantities of phosphate (P) and zinc (Zn) for grain yield and Zn accumulation, a pot study was conducted. Using a completely randomized design (CRD) factorial combination, two Zn levels (0, 5 kg ha-1) and three P levels (0, 90, 120 kg ha-1) were applied. The study revealed that wheat genotypes responded differently to varied doses of P and Zn. In order to considerably increase yield (grain and straw) and related features (number of spikelets per spike, spike length, number of grains per spike, and 1000-grain weight), the combination of 5 kg Zn and 90 kg P ha-1 was used. The most effective combination for all wheat genotypes was found to be 5 kg Zn + 90 kg P ha-1, which significantly increased grain yield (23 %) and related attributes including number of spikelets per spike (11 %), spike length (10 %), number of grains per spike (17%) and 1000-grain weight (10 %) as compared to control. In general, all wheat genotypes showed decreased mycorrhizal root infection (205-290 %) and grain zinc uptake (22-45 %) when exposed to a higher dose of P (120 kg ha-1) and vice versa. The genotype TD-1 had the higher agronomic Zn efficiency (139 % and 235 %) at 5 and 90 kg Zn and P ha-1 as compared to other genotypes NIA-WR1 and Chakwal-86 respectively. Similarly, it was proved that applying 5 kg of Zn ha-1 along with P level of 90 kg ha-1 was the best combination to maximize the potential yield of Chakwal-86. Among the genotypes, NIA-WR1 accumulated the higher quantity of Zn (51 % and 40 %) in its grains at 5 kg Zn along with 90 kg P ha-1 compared to the genotypes TD-1 and Chakwal-86 respectively. The same genotype, NIA-WR1 accumulated the maximum Zn (44.26 µg g?¹) in its grains even without the application of exogenous Zn. This indicates that it is Zn-efficient and capable of thriving in Zn-deficient soils. It can utilize the Zn present in the native soil to achieve a high zinc content in its grains. However, further research is needed before general recommendations can be made for wheat growers.

Techno-economic and sustainable energy solution for small industrial estate of Gujranwala, Pakistan-using hybrid microgrid

Amir Ali Shah — 2025-01-02

This research comprises the energy solutions for small industrial estate Gujranwala, Pakistan using HOMER pro simulation software and considering the best possible energy plan with maximum reliability, cost-effectiveness, efficiency and availability. Small industrial estate is an industrial zone comprising small industrial units (ceramics, cutlery, sports goods, surgical instruments, tough tile, garments, etc.) up to a maximum energy consumption of 500KW each, organized by provincial government of Punjab, Pakistan. In this research, we have presented an optimized hybrid microgrid framework to ensure a consistent and reliable energy supply for the small industrial estate of Gujranwala, Punjab, Pakistan. In this research work, we have collected the required data related to the availability of natural energy resources in the Gujranwala region like solar DNI (direct normal radiations), GHI (global horizontal irradiations) wind speed and temperature from NREL official website. Then We have estimated the maximum power demand by collecting real-time one-year data from the existing 132kv Shaheenabad grid, Punjab, Pakistan, which is feeding electric power to an existing small industrial state Gujranwala similar to a new industrial estate under construction. We have proposed an optimized hybrid microgrid design by integrating that collected data into HOMER Pro simulation application including electrical load profiles, solar irradiance data, regional temperature profile, diesel costs, PV module longevity, degradation rates, efficiency, costs of PV modules, national grid energy pricing and net metering with national grid. A comprehensive assessment of the proposed optimal system involves technical and economic long-term benefits for small industrial estate Gujranwala, Pakistan.

Synthesis and release profile of ibuprofen-loaded zein and gelatin nanofiber scaffolds for potential transdermal application in burn wound treatment

umaima saleem memon — 2025-01-02

An antimicrobial barrier may accelerate rehabilitation by facilitating the healing of living tissues affected by injuries caused by burning. Fibrous mats based on nanotechnology have been extensively researched for their potential as drug delivery systems. The fabrication of electrospun polymeric nanofibrous mats containing non-steroidal anti-inflammatory medications (NSAIDs) and antibacterial agents has been discussed in this article. Electrospinning has been employed to create nanofibrous mats from pure zein and pure gelatin, and their combined use with Ibuprofen, an NSAID. Due to the ability of these electrospun nanofibrous mats to control exudation, they keep the site dry and shield it from microbiological activity, which makes them a good option for wound healing. In addition to providing an antibacterial layer that promotes wound healing, the manufactured mats can also function as medicine transporters. This research article extensively addresses the drug release profile from the carrier nanofibrous mats and the characteristics of fiber mats using standard characterization techniques like Fourier-transform infrared spectroscopy (FTIR) and Scanning electron microscope (SEM). The resultant fiber mats' drug release kinematics are compared to the standard mathematical models (Korsmeyer-pappas and Higuchi. The cumulative drug percentage released from these mats consistently validated Higuchi’s model, which exhibited diffusion-controlled super case-II transport (n>1). The results indicate that the Ibuprofen is efficiently loaded onto the nanofibers, with a uniform distribution of the drug throughout the fiber matrix and ensures that the drug is released in a controlled and sustained manner, promoting effective wound healing.

Innovative use of brick waste for eco-friendly treatment of indigo dye house wastewater: An approach towards waste to resource

Hafiz Usama Imad — 2025-01-02

The textile industry is the backbone of Pakistan’s economy, holding a share of 57% in total exports of Pakistan but also possessing several environmental challenges due to the discharge of highly contaminated wastewater. On the other hand, construction and demolition waste is another threat to the environment because of its massive quantity and large area requirement for its disposal. This study explores the use of waste bricks as a sustainable medium to treat textile wastewater. Waste bricks, sourced from construction debris in Hyderabad, were crushed, cleaned, and used in the treatment system. Composite samples of textile effluent were collected from a textile factory in Nooriabad, Sindh, and treated in a brick column treatment system over 14 days. Key parameters such as pH, turbidity, total dissolved solids (TDS), total suspended solids (TSS), hardness, chloride concentration, chemical oxygen demand (COD), dye concentration, electrical conductivity (EC), and total solids (TS) were monitored using standard methods. The results revealed that the waste brick filtration system effectively reduced various pollutants within four2 days and improved further, with significant decreases in COD (94%), dye concentration (99%) , and turbidity (91%) after 14 days, indicating that waste bricks can be a green alternative for wastewater treatment, promoting the concept of “using waste to treat waste” and contributing to environmental sustainability.

Effect of polyester fiber on compressive strength and split tensile strength properties of high strength concrete

Mohsin Ali — 2025-01-02

High strength concrete (HSC) is essential for large-scale projects, such as bridges, dams, and high-rise buildings, as it allows for reduced dimensions of beams and columns. This experimental study investigates the effect of polyester fiber on the compressive and split tensile strength properties of HSC grade C60. A total of 90 concrete mixes were prepared, incorporating polyester fiber at varying volumes of 0%, 0.2%, 0.3%, 0.4%, and 0.5%, with curing periods ranging from 3 to 28 days. The results demonstrate that the inclusion of polyester fiber significantly enhances the mechanical properties of HSC, with optimal compressive strength achieved at 0.3% fiber content, leading to a 14.17% increase compared to the control mix. Additionally, split tensile strength improved by 40.63% at 0.2% fiber content and by 65.68% at 0.3% fiber content. Although the slump decreased by 14% to 57% with increased fiber content, no bleeding or segregation was observed in any of the mixes. These findings underscore the potential of polyester fiber to improve the performance of high strength concrete in structural applications.

Assessing excavatability in varied rockmass conditions using real-time data and machine learning technique

Shafi Muhammad Pathan — 2025-01-02

This study investigates shovel excavation performance across various rockmass conditions by integrating real-time performance assessments, rockmass property analysis, and machine learning techniques. Correlation analysis revealed significant positive relationships between Total Loading Time (TLT) and selected rock properties, specifically uniaxial compressive strength (UCS), tensile strength (TS) cohesion (C), and moisture content (M), while a negative correlation was observed with wet bulk density (WBD). Pareto analysis further highlighted C, UCS, and TS as the most impactful factors, cumulatively accounting for 56% of the total effect on excavation performance. A multiple linear regression model, using TLT as the dependent variable and significant rock properties (C, UCS, M) as predictors, achieved a strong correlation (R=0.76) and explained 76% of the variance, demonstrating the model’s effectiveness in estimating shovel performance. K-nearest neighbors (KNN) classification, optimized with a k-value of 7 and Manhattan distance, achieved a high accuracy of 99.43% in categorizing the excavation difficulty into four distinct classes. The frequency distribution of TLT data indicates that most materials in the pit fall under “Very Easy” and “Easy” classes, simplifying excavation processes. This research underscores the importance of the key rock properties in evaluating the excavation performance predictions and support optimized operational strategies in mining. Future work could expand on these findings by using additional machine learning techniques and exploring non-linear models to capture complex relationships.

Validating aggregates and bitumen characteristics in road maintenance and safety management for motorizing countries using multidimensional diagrammatic models

Khawaja Sheeraz — 2025-01-02

Maintenance and management systems are essential parameters that align with traffic safety. The dependent factors include traffic accidents on blackspots, contributory causes like pavement and geometric features, and human or vehicle issues. As per the research, more than 75% of accidents are reported due to the deteriorated conditions of the roads in a metropolitan city, Karachi, Pakistan. The consistent feature is the lack of consideration in the adopted strategies of the city, which are based on routine and periodic maintenance procedures. Unfortunately, Karachi, which has an extensive infrastructure, is seriously affected with road delaminations and cracks. Due to limited budget requirements, the city's infrastructure is deficient in pre and post-maintenance techniques. For post phases, it is required to establish the confirmatory experimental protocols for pavement sections to which road failures are observed after initial surveys and investigations. It may be directly associated with the aggregates and bitumen testing on sections of blackspots. It is evident from the statistics that around 45% to 65% of the sections are being influenced by pavement characteristics while evaluating the existing road cracks. It is also compared with the design guidelines of similar sections to identify the gaps in the existing data. The novelty in the paper deals with the usability of accident and failure data for verification of material characteristics conditioned to the industry data. The verification data entails the standard properties of asphalt. The paper enables the working of diagrammatic models in which multidimensional factors, connections of model parameters, and recommendations are elaborated.