Pavement management systems

Eggshell is an agricultural waste which has been found to possess pozolanic property and has been used as partial replacement of cement in other materials. It is against this background that it is been used as a stabilizing agent on problematic lateritic soil. The laterite soil was obtained from a borrow pit at Ajegunle along Ilaro-Papalanto road, Yewa South Local Government Area in Ogun State, Nigeria. The borrow pit site lies within the coordinates 6°53 ' 11.81 " N and 3°7 ' 44.88 " E.The eggshells were obtained from Obasanjo Farms Nigeria Limited, Ota, Ogun State, Nigeria and grounded to powder. In line with BS 1377 (1990) and other relevant codes of practice, consolidation tests on 1hour and 24hours soaked samples were conducted to determine the relevant consolidation parameters and settlement indices. Eggshell powder was substituted in the lateritic soil (sedimentary formation) in range of 0% to 50% with 0% serving as control experiment for both the 1-hour soaked and 24-hour soaked samples. The results indicated that for the 1hour soaked sample, the value of the coefficient of consolidation is within the range of 1.22 at 50% substitution to 1.68 at 10% substitution, while the range is 0.63 at 50% substitution to 1.60 at 20% substitution. Finally, from the results, coefficient of consolidation reduces as the percentage eggshell powder substitution increases.

Pavements are major means of highway infrastructure. Maintenance and rehabilitation of these pavements for the required serviceability is a routine problem faced by highway engineers and organizations. Improvement in road management system results in reduction of time and cost, the pavement condition survey plays a big role in the pavement management. The initial phase in setting up a pavement management system (PMS) is road network identification. A vital element of a PMS is the capacity to assess the present condition of a pavement network and anticipation of future condition. The pavement condition index (PCI) is a numerical index generally utilized for the assessment of the operational condition & structural reliability of pavements. Estimation of the PCI is dependent on the results of a visual inspection in which the type, severity, and quantity of distresses are distinguished. In this research, a pavement distress condition rating strategy was utilized to accomplish the goals of this study. The main targets of this research were to categorize the common types of distress that exist on "Lakhi Larkana National Highway (N-105)", and to estimate the pavement condition index. Using these data, Average PCI for the highway section was calculated. PCI to assess the pavement performance, 10 out of 19 defects were recognized in the pavement, as stated by the PCI method. Results indicated that the common pavement distress types were depressions, polished aggregate, rutting, potholes, block cracking, and alligator cracking.

This study aimed to evaluate the capacity of the YOLOv8 algorithm to detect potholes, patches, and cracks. To achieve this, a section of a highway was recorded, manually evaluated in the field, and compared with a semi-automatic evaluation based on video processing by the model. The model yielded different results from those obtained through field assessment. Although only a portion of the Maintenance Condition Index is used in the assessment, this marks the first use of an index integrated with YOLOv8. Thus, it is concluded that the model requires further improvements to become viable for definitive application.

Soft ground improvement to provide stable foundations for infrastructure is national priority for most countries. Weak soil may initiate instability to foundations reducing their lifespan, which necessitates the adoption of a suitable soil stabilization method. Amongst various soil stabilization techniques, using appropriate admixtures is quite popular. The present study aims to investigate the suitability of bagasse ash and stone dust as the admixtures for stabilizing soft clay, in terms of compaction and penetration characteristics. The studies were conducted by means of a series of laboratory experimentations with standard Proctor compaction and CBR tests. From the test results it was observed that adding bagasse ash and stone dust significantly upgraded the compaction and penetration properties, specifically the values of optimum moisture content, maximum dry density and CBR. Comparison of test results with available data on similar experiments conducted by other researchers were also performed. Lastly, a study on the cost effectiveness for transport embankment construction with the treated soils, based on local site conditions in the study area of Assam, India, was carried out. The results are analyzed and interpreted, and the relevant conclusions are drawn therefrom. The limitations and recommendations for future research are also included.

Cold rolling is widely recognized as a key industrial process for enhancing the mechanical properties of materials, particularly hardness, through strain hardening. Despite its importance, accurately predicting the final hardness remains a challenge due to the inherently nonlinear nature of the deformation. While several studies have employed artificial neural networks to predict mechanical properties, architectural parameters still need to be investigated to understand their effects on network behavior and model performance, ultimately supporting the design of more effective architectures. This study investigates hyperparameter tuning in artificial neural networks trained using Resilient Backpropagation by evaluating the impact of varying number of hidden layers and neurons on the prediction accuracy of hardness in 70-30 brass specimens subjected to cold rolling. A dataset of 1000 input-output pairs, containing dimensional and hardness measurements from multiple rolling passes, was used to train and evaluate 819 artificial neural network architectures, each with a different configuration of 1 to 3 hidden layers and 4 to 12 neurons per layer. Each configuration was tested over 50 runs to reduce the influence of randomness and enhance result consistency. Enhancing the network depth from one to two hidden layers improved predictive performance. Architectures with two hidden layers achieved better performance metrics, faster convergence, and lower variation than single-layer networks. Introducing a third hidden layer did not yield meaningful improvements over two-hidden-layer architectures in terms of performance metrics. While the top three-layer model converged in fewer epochs, it required more computational time due to increased model complexity and weight elements.

A common phenomenon in developing countries is that the function of the pavement in the road network will experience structural damage before the completion of life is reached, and the uncertainty of pavement damage is difficult to predict. Planning for maintenance treatment depends on the accuracy of predicting future pavement performance and observing current conditions. This study aims to apply the Markovian probability operational research process to develop a decision support system predicting future pavement conditions. Furthermore, it determines policies and effectiveness in managing and maintaining roads. A standard approach that can be used by observing the history of pavement damage from year to year is to estimate the transition probability as a Markovian-based performance prediction model. The results show that the application of the model is quite optimal, changes in pavement conditions after repair can be easily compared with an increase in good condition, reaching 92.8%. Routinely and consistently handling road deterioration will give favorable results regarding pavement condition value. This will ease in the management of the road network and the accomplishment of the optimal maintenance and repair policies.

The development of a nation partially depends on sustainable materials obtained from agricultural products. Increased agricultural products could increase the amount of waste generated yearly. This paper presents a review on the use of agricultural waste with pozzolanic properties (rice husk ash, Locust bean waste ash, Palm oil fuel ash, Banana leaf ash, Bagasse ash, Coconut shell ash, Bamboo leaf ash, Corn cob ash, Cassava peel ash and Palm kernel shell ash) in various geotechnical engineering applications. Interestingly, these wastes were subjected to various laboratory tests such as (particle size distribution, Compaction, Atterberg, unconfined compressive strength (UCS) and California bearing ratio (CBR)) to assess their effectiveness in soil improvement. In all these, the percentages of the materials required for soil improvement were discussed. The reports from various researchers have shown that agricultural waste having pozzolanic properties improves the engineering properti...

This paper presents the history of the application of a pavement management system (PMS) in Vietnam and the background to the successful development of a new PMS system that was customized from the Kyoto Model 1),2) to make it suitable for local application. In addition, we report on a scheme for road asset management with sustainable cooperation between Japan and Vietnam, realized through the support of the Japan International Cooperation Agency as well as the contributions of many Japanese researchers and experts over a long period of time. Numerous efforts, both personal voluntary-based and organizational-based, have been made to enable the adequate transfer of technologies and knowledge, and to ensure that the capacity for road asset infrastructure management and maintenance is enhanced in a sustainable way. We are pleased to say that fruitful outcomes have been made from the perspectives of circumstance, location, and enhancement of human capacity. The approach adopted is also expected to be expanded to other fields, regions, and nations in a wider scope.

The lack of sufficient, accurate and time series dataset on road pavement surface condition in most developing countries over time has posed a serious challenge to highway engineers in terms of developing reliable pavement management system. This study examined the trend of flexible road pavement behaviour and surface condition classification from a historic dataset using intelligent algorithms such as; Random Forest (RF), Decision Tree (DT) and Naïve Bayes (NB) theory implemented in the Waikato Environment for Knowledge Analysis (WEKA) software. Some randomly selected highway links were considered for the investigation. The surface condition attributes considered included the percentage of fatigue cracks, average rut depth and drainage condition in a relatively comprehensive database obtained from the Federal Ministry of Power, Works and Housing Nigeria. Results of the analysis revealed the performance trend and classification of road pavement surface conditions based on surface distresses. The analysis indicated that, the RF and DT algorithms yielded accurate classification against the NB algorithm which could not handle instances of missing data efficiently, hence incurred classification errors. The use of experience-based models that could handle challenges of missing data in a dataset was recommended for the development of road pavement management system using such database.

This study investigates the perceptions of public transport drivers in Gaborone, Botswana, regarding the causes, impacts, and accountability for recurring potholes on urban roads. Persistent road deterioration poses significant challenges to mobility, vehicle maintenance costs, and operational efficiency, yet the perspectives of high-frequency road users remain underrepresented in existing literature. Using a quantitative, cross-sectional design, the research surveys 353 taxi, combi, and bus drivers operating from the Gaborone Bus Rank. It examines drivers’ views on (1) the primary causes of potholes (e.g., substandard construction, material quality, drainage failures); (2) the economic and safety impacts on their livelihoods; (3) the accountability of construction companies and government agencies; and (4) proposed solutions for improving road maintenance and contractor oversight. Findings aim to inform infrastructure policy, enhance contractor accountability, and reduce operational burdens on public transport systems. The study addresses a critical gap by centering the experiences of frontline road users in Botswana’s urban transport resilience discourse.

Despite advancements at the state level, many local highway agencies administering county roads lack an established pavement management system. This is often due to insufficient resources, technical expertise, and challenges related to data availability and quality. This research paper introduces a hybrid system for prioritizing pavement maintenance and rehabilitation (M&R) of Wyoming county roads, integrating pavement condition data, expert perspectives, and road characteristics. The Analytic Hierarchy Process (AHP) was employed to compute the relative significance of pavement conditions and various road characteristics. The results reveal that traffic volume is the most influential factor, accounting for 27% of the overall importance, underscoring its critical role in M&R prioritization. Speed limit and land use also play significant roles, each contributing approximately 20% to the decision-making process. The findings reveal a preference for prioritizing pavement condition over road characteristics, with a correlation coefficient of 0.85 between rankings based solely on pavement condition and those derived from the hybrid system. A 20% discrepancy was observed between the top third pavement segments identified by the two approaches, with even greater differences noted in the second-top-third segments. This research provides a robust framework that not only assists Wyoming counties in effective and efficient road management but also offers valuable guidelines for other states to enhance their pavement M&R strategies.

The pavement is a complex construction subject to a range of environmental and loading conditions. Transportation organizations use pavement management systems (PMSs) to maintain satisfactory pavement performance. The pavement condition index (PCI) is a commonly used performance indicator, yet PCI evaluation is costly and
time-consuming. Machine and deep learning algorithms have recently been more instrumental for forecasting pavement conditions. This research uses AI tools to develop a correlation between PCI and collected distress in urban road networks. The distresses for 15,000 pavement segments in Egypt were investigated through a desk study and field data
collection. To this end, several machine learning (ML) and deep learning approaches were developed. The ML techniques include random forest (RF), support vector machine (SVM), decision tree (DT), and the deep learning approach entails artificial neural networks (ANN). The proposed techniques provide precise PCI estimates and can be seamlessly integrated with PMCs using ubiquitous spreadsheet programs. The results have shown excellent predictions of the ANN model, as demonstrated by the high coefficient of determination (R2 = 0.939) and the low root mean squared error (RMSE = 7.20) and the mean absolute error (MAE = 2.94). This study sets out to provide a reliable and affordable alternative to specialized tools like MicroPAVER. The ANN model exhibited greater prediction accuracy than the other developed models.

Taylor & Francis makes every effort to ensure the accuracy of all the information (the "Content") contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.

Freight transport in South Africa is dominated by two modes, namely road transport and rail transport. The current dominance of road is not only linked to high value commodities, perishables or short transport distances, but has recently been extended to include freight transport of bulk low value goods such as coal and iron ore, previously the domain of rail transport. The condition of the rural road network is deteriorating rapidly as traffic volumes and axle loads increase on an aging network.

One of the main components of pavement management system (PMS) is pavement evaluation. Several indices have been defined for the evaluation of existing pavement. The Pavement Condition Index (PCI) is a common index used for pavement evaluation. In order to calculate PCI, a significant volume of condition data-based on distress surveying-is required. The objective of this research is to reduce the volume of required data by introducing a new sample unit definition. For this reason, "wheel path sample units" were defined and used instead of the standard sample unit (according to ASTM D6433). The analysis of results showed that not only there is no significant difference between standard and wheel path PCIs, but also there is a good correlation between standard PCI and both wheel path PCI (PCIw) and outside wheel path PCI (PCIow), corresponding to R 2 = 0.929 and R 2 = 0.874, respectively. Also, PCIow saves a great amount of time and energy.

The poor geotechnical characteristics of soft clay soils are worldwide problems that pose several challenges for extensive damage to structures if not adequately treated. The purpose of this investigation was to evaluate
the suitability of quarry dust and cement for stabilization of Niger Deltaic Marine clay. The soil sample was classified in the laboratory, and initial findings indicate that the soil falls under the AASHTO classification system's A-7-5 class. The Moisture Content test, Sieve Analysis test, Atterberg limit test, Specific Gravity test, Proctor Compaction test, CBR
test, and Triaxial test are a few geotechnical laboratory tests used to assess the geotechnical characteristics of the soil. A concentration of 1% to 5% cement and 0% to 20% quarry dust was used to stabilize the
soil. The analysis showed that the Quarry Dust and cement increased the maximum dry density (MDD) of the soil and decreased the optimum moisture content (OMC). The additives also improved the CBR but did meet the minimum requirement for CBR for the soil in Nigeria. When the additives were combined, the soil geotechnical properties improved further. The highest value for CBR was (14.15%) and cohesion (88kPa) occurred at 20% quarry dust and 5% cement. The highest MDD occurred at 20% Quarry Dust and 5% cement with an MDD value of 1410kg/𝐦𝟑 when both additives were combined.

The project that is the subject of this report was done under contract for the Virginia Department of Transportation, Virginia Transportation Research Council. The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Virginia Department of Transportation, the Commonwealth Transportation Board, or the Federal Highway Administration. This report does not constitute a standard, specification, or regulation. Any inclusion of manufacturer names, trade names, or trademarks is for identification purposes only and is not to be considered an endorsement. Each contract report is peer reviewed and accepted for publication by staff of Virginia Transportation Research Council with expertise in related technical areas. Final editing and proofreading of the report are performed by the contractor.

This report describes research conducted to incorporate pavement structural condition information obtained from the Traffic Speed Deflectometer (TSD) into the Virginia Department of Transportation (VDOT) pavement management system decision making process for bituminous pavement sections. Testing was conducted on a 4,000-mile (1,500 interstate miles and 2,500 primary roads miles) subset of the VDOT network. The report showed that the pavement structural condition, as measured by the TSD, has an impact on the rate of deterioration of the pavement surface. In addition, for the set of collected data, the consistency between the TSD and Falling Weight Deflectometer (FWD) in identifying the same weak sections was found to be higher than the consistency between repeated sets of FWD measurements performed in the Bristol district. The consistency was defined as the percentage of structurally weak sections identified by both devices as a proportion of the number of weak sections. Also, the di...

Soils with a significant proportion of clay or silt tend to have poor geotechnical characteristics (such as volumetric change and low strength) when exposed to varying moisture contents. The chemical stabilizers currently used are costly and have negative effects on the environment. Arkansas, in the United States, is the leading producer of rice in the country. During rice production, ~20% of the harvested grain consists of rice husk (RH). The rice milling process produces ash with a considerable amount of silicate. Research is being conducted to address the environmental concerns associated with its disposal in Arkansas, as there is growing interest in using rice husk ash (RHA) as a stabilizing additive for problematic soils. For RHA to be effective, an activator such as lime may be needed. The present study focuses on investigating the effectiveness of RHA in stabilizing substandard soils, as no prior study has used RHA as a stabilizing agent in the United States. Additionally, the research aims to determine the optimal proportions of RHA, hydrated lime (HL), or a combination of both. The study employs varying percentages of RHA (3%, 6%, and 9%) and HL (1%, 3%, and 5%) in a unary system. In the binary system, 3%, 4%, and 5% of RHA are combined with 1% HL. The study includes a range of routine tests, such as assessments of Atterberg limits, modified proctor compaction, pH levels, California bearing ratio (CBR), unconfined compressive strength (UCS), and free swell (FS). Furthermore, results from scanning electron microscopy (SEM) coupled with X-ray diffractometer analyses demonstrated significant improvements in UCS and CBR values for the treated soils, indicating enhanced strength and stability. Moreover, the FS test reveals a reduction in swelling characteristics for the stabilized soils. It was found the optimal dosages of RHA and HL were 6% and 3%, respectively. Notably, when RHA and HL were combined, a blend of these two stabilizing agents exhibited the most promising results in terms of improving the swelling. The optimum percentage of the combined stabilizing agents was 4% RHA + 1% HL. The findings of this proof-of-concept research hold substantial potential for the construction industry in Arkansas, offering cost-effective alternatives to conventional soil stabilization methods while promoting the sustainable and environmentally conscious use of RHA.

Mustafa Alas, for their invaluable guidance, constructive feedback, and continuous support throughout this research. Their expertise and encouragement have played a crucial role in shaping this work. I am also grateful to the faculty members of the Department of Civil Engineering at Near East University for their insightful lectures and academic support during my studies, which have enriched my understanding and helped me develop this research. A special thanks to my family and friends for their unwavering support, motivation, and patience during this challenging yet rewarding process. Their encouragement has been a source of strength throughout my academic journey. At the end, I extend my appreciation to everyone who contributed directly or indirectly to this research, whether through technical assistance, discussions, or moral support. Your help and encouragement have been truly invaluable.

Concrete is predominantly made up of cement. Cement manufacturing results in the emission of a large quantity of carbon dioxide in the air. Use of fly ash in concrete is advantageous for the environment and provides lasting durability. By utilizing a reduced amount of cement, it contributes to the conservation of materials such as limestone and coal that are essential for cement production. While it remains ever-present and never diminishes, generating it requires a considerable amount of power. So, it's a good idea to use cement carefully. Concrete is a durable substance can be moulded into various forms. It is frequently employed in construction for its multifunctionality. Finding a good replacement for cement in concrete is really important because we don't want to make the quality of the concrete worse. A powdery substance known as fly ash contains a significant quantity of cement. The study investigated the replacing other materials with fly ash on the M30 grade high strength concrete. We tested different quantities of cement, such as no cement, 10%, 20%, and 30%. Incorporating a larger amount of fly ash in the concrete mix creates a more manageable and robust material. Furthermore, it's been discovered that the inclusion of higher proportions of fly ash results in a reduction of concrete durability.

This report focuses on pavement performance and treatment models for Louisiana Department of Transportation and Development (LADOTD) and is in continuation of Louisiana Transportation Research Center (LTRC) Report No. 430 "Development of Uniform Sections for Pavement Inventory." During the course of the study, a comprehensive evaluation and analysis of the pavement performance prediction and treatment models were conducted using LADOTD pavement distress data and historical information. Statistical analyses were used to generate models for pavement condition prediction and treatment performance evaluation based on the "surface age" or the "age" of pavements. These prediction models were developed for each distress type for four pavement types and four highway system classifications. The developed models provided good predictions and helped determine the remaining service life and pavement network health evaluation. Further research study is required to conduct comprehensive analyses of pavement treatment selection models based on LADOTD distress data and a scheme for the selection of the most cost-effective pavement treatment/preservation action based on the treatment performances and causes of distress. It is believed that findings of this study will enhance LADOTD's capabilities in predicting pavement performance and monitoring pavement network condition in a most efficient and cost-effective manner.

There is a growing global interest in preserving transportation infrastructure. This necessitates routine evaluation and timely maintenance of road networks. The effectiveness of pavement management systems (PMSs) heavily relies on accurate pavement deterioration models. However, there are limited comparative studies on modeling approaches for rural roads in arid climatic conditions using the same datasets for training and testing. This study compares three approaches for developing a pavement condition index (PCI) model as a function of pavement age: classical regression, machine learning, and deep learning. The PCI is a pavement management index widely adopted by many road agencies. A dataset on pavement age and distress was collected over a twenty-year period to develop reliable predictive models. The results demonstrate that the regression model, machine learning model, and the deep learning model achieved a coefficient of determination (R2) of 0.973, 0.975, and 0.978, respectively. While these values are technically equal, the average bias for the deep learning model (1.14) was significantly lower than that of the other two models, signaling its superiority. Additionally, the trend predicted by the deep learning model showed more distinct phases of PCI deterioration with age than the
machine learning model. The latter exhibited a wider range of PCI deterioration rates over
time compared to the regression model. The deep learning model outperforms a recently
developed regression model for a similar region. These findings highlight the potential of
using deep learning to estimate pavement surface conditions accurately and its efficacy in
capturing the PCI-age relationship.

The predominant type of pavement currently in use is flexible pavement constructed with a hot asphalt mixture. However, with economic development advancing in various regions, transportation challenges, such as vehicle
overloading,
especially in developing nations, have emerged. It is crucial to
enhance and innovate the materials used in asphalt mixtures, which form the primary structure supporting pavement loads, to address these issues effectively.
Incorporating green materials sourced from natural and waste sources becomes essential for promoting sustainable development. This study investigates the utilization of materials derived from the sap of the lannea coromandelica tree, known for its natural resin-like adhesives, as well as coal waste as a filler in asphalt mixtures. Laboratory experiments were conducted alongside vehicle load data analysis, focusing on excessive vehicle loads on roads. The findings indicate that incorporating these green materials enhances the modulus value of the asphalt mixture, particularly in the lannea coromandelica gum with fly ash (LCGFA) mixture, which amounted to 4161 Mpa, followed by the lannea coromandelica gum with bottom ash (LCGBA) mixture by 3729 Mpa, compared to the control mixture, hot mix asphalt concrete. Additionally, these results influence the Truck Factor value of vehicles. The higher the truck factor value, the impact on the pavement structure is due to the vehicle load transmitted through the vehicle axis, which is forwarded by the pavement to the subgrade. The adoption of LCGFA and LCGBA reduces the Truck Factor value from 5,386 to 4,980 under the LCGFA mix and 5,143 under the LCGBA mix, indicating decreased pavement damage caused by the wheel axles of overloaded vehicles. Consequently, utilizing these green materials is considered capable of providing enhanced durability and
environmental benefits to pavement structures. In addition, this material can be used by field practitioners to reduce material costs and environmental impact.

Clayey soils often showed undesirable engineering behavior such as low bearing capacity, swelling and shrinkage characteristics. However, chemical improvement, thermal improvement and improvement by additives like lime, cement and sand offer an efficient technique to overcome the problems resulting from Expansive soils. This paper presents a review of the swelling behavior of sand-clay mixtures as well as the effect of sand on the physical and mechanical characteristics of expansive soils. Results highlight the importance of sand in improving the behavior of expansive soils. Finally, the most important general conclusions about the behavior of expansive soils and suggestions for future researches are highlighted.

Loess soils have always been among the most problematic soils, according to engineers. Due to the sensitivity and vulnerability of this soil type, researchers are constantly looking for different solutions to improve and stabilize this soil using different methods. In the present study, a mixture of lime and rice husk ash (LRHA) was used to modify the loess soil of the Golestan province, Iran. Lime and RHA were mixed in a ratio of 2:4 and with different contents of 2.5, 5, 7.5, 10 and 12.5% of the dry weight of the soil. To study the effect of curing on the strength properties, the samples were cured and tested for 7, 18 and 28 days. Atterberg limits, modified Proctor, unconfined compressive strength (UCS) and scanning electron microscope tests (SEM) were performed. It was found that increasing LRHA content increased the value of UCS at different curing times, such that UCS increased about five times by the addition of 5% LRHA to the loess at the end of 28 days. As the curing time increased, the optimum amount of the additive also decreased. The results showed that combining the soil with LRHA is an effective method for improving and stabilizing loess soil. According to the results of the study, the optimum value of 5% additive is economically and technically recommended.

Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R 2 , MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM model.

Prediction models in mobility and transportation maintenance systems have been dramatically improved by using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well as their hybrids, i.e., Levenberg–Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through us...

The volume of wastes generated in the world over has increased over the years due to increase in population, socioeconomic activities and social development. These wastes come from agricultural, industrial, commercial as well as construction activities. One of the most attractive options of managing such wastes is to look into the possibility of waste minimization and re-use. This research is aimed at assessing the impact of coconut husk ash (CHA) on the stabilization of poor lateritic soil deposit found at Otu in Itesiwaju Local Government Area in Oyo State, Southwestern Nigeria. In achieving this aim, Lateritic soil was collected from Otu in Oyo State. The soil was air dried and divided into six samples and each sample was stabilized using 0, 2, 4, 6, 8 and 10% of coconut ash by mass of soil sample. The samples were subjected to the following laboratory soil tests: particle size distribution analysis, Atterberg limit test, compaction test, and California Bearing Ratio in accordanc...

The volume of wastes generated in the world over has increased over the years due to increase in population, socioeconomic activities and social development. The waste that comes from agricultural, industrial, commercial as well as construction activities are composed of a very wide variety of materials such as food wastes, construction waste, paper, plastic and other discarded residual items. One of the most attractive options of managing such wastes is to look into the possibility of waste minimization and re-use. This research is aimed at assessing the effects of

This study assesses the geotechnical properties of lateritic soil stabilized with the ashes of oil palm fronds. These properties are then compared with those of the same soil stabilized with cement to determine how well the ashes perform since cement is considered to be the best stabilizer. Laboratory tests such as specific gravity, moisture content, Atterberg limits, particle size distribution, compaction, unconfined compressive strength (UCS) and California bearing ratio (CBR) tests were first carried out to determine the basic properties of the lateritic soil (without the stabilizers). Based on the results of these tests, the soil was classified according to AASHTO soil classification system as an A-7-5 soil which is a poor soil. Hence, the need for stabilization. Thereafter, strength tests such as California bearing ratio (CBR), unconfined compressive strength (UCS) and compaction tests were performed on the soil to which the ashes and cement were added in percentages of 2, 4, 6, 8 and 10 by weight of the lateritic soil. The compaction test showed that the highest maximum dry densities (MDD) were recorded in the case of the oil palm frond ash (OPFA) and cement at 4% (MDD = 2.02kg/m 3) and 6% (MDD = 2.40kg/m 3) respectively. The highest CBR values obtained were 32.6% and 87.32% at 4% OPFA content and 6% cement content respectively.The unconfined compressive strengths (UCS) of the soil were highest at 4% OPFA content (234.86kN/m 2) and 6% cement content (588.32kN/m 2). The chemical tests performed on the OPFA and the cement showed that the highest oxide component were SiO 2 (33.67%) and CaO (60.83%) respectively.

Expansive soil presents considerable difficulties in building and geotechnical engineering due to its adverse volume alterations in response to moisture fluctuations. This research examines the application of coir fiber and fly ash as stabilizers for expanding soils. Coir fiber, a natural and biodegradable substance, and fly ash, a byproduct of coal combustion, were evaluated for their capacity to mitigate soil expansion. Comprehensive soil samples obtained from the Korean Export Processing Zone (KEPZ) underwent laboratory analysis. Per USCS classification, the soil is categorized as inorganic highly plastic clay (Liquid Limit= 51.14%, Plastic Limit= 26.07%, Plasticity Index= 25.07%) and as moderate to very expansive soil (Liquid Limit= 51.14%, Plastic Limit= 26.07%, Plasticity Index= 25.07%). The soil was amended with different proportions of coir fiber (0.25%, 0.5%, 0.75%, 1%) and fly ash (3%, 6%, 9%, 12%), followed by a series of standardized tests, including compaction and unconfined compressive strength assessments, to analyze the alterations in soil characteristics. The results indicated that incorporating coir fiber and fly ash substantially enhanced the engineering features of expansive soils. Coir fiber enhanced shear strength, reduced liquid limit, and augmented soil cohesion. Conversely, fly ash functioned as a pozzolanic substance, facilitating cementitious reactions and thereby enhancing soil stability. The combination of coir fiber and fly ash exhibited a synergistic effect on soil workability, plasticity, and strength.

With the general lack of equivalence between the many methods and measures by which road roughness is characterized, standardized indices offer the means to achieve a time-stable data base that can be utilized by all. The International Road Roughness Experient (IRRE) was organized in Brasilia, Brazil, to find a suitable index and to quantify the relationships between different equipment and roughness indices in use. Roughness measurements were made on 49 test sites by diverse types of equipment in common use. The data were analyzed to determine the equivalence between the roughness measures that could be obtained with each type of equipment and whether one common measure was applicable to all. The results from the IRRE showed that a standard roughness index is practical and measurable by most of the equipment in use today, whether of the profilometer or road meter type. As a result of the IRRE, a standard index was selected that is based on the quarter-car analysis method with stand...

The northernmost highway district of Lapland, Finland, differs considerably from other regions in Finland. Its road network is very long, about 9000 km, whereas traffic volumes are low because of an extremely low population density. The arctic climate puts severe restrictions on the technology used, the amount and quality of winter maintenance, and road standards. To fully comprehend the implications of proposed budget cuts, the district initiated an analysis on the impacts of alternative strategies. A network-level pavement management system was used to allocate and optimize funds for rehabilitation and to analyze the development of the condition of the network with nonoptimal funds. Summer and winter maintenance were analyzed by an analytical hierarchical process. As to investments, in addition to a traditional cost-benefit analysis, indirect economic effects were studied with a regionalized input-output model. According to the results the pavement condition targets cannot be achi...

This study focused on investigating alternative construction
materials for low-cost housing due to the skyrocketing costs of
conventional building materials. The study methodology includes
laboratory evaluation of the characteristics of
150mm×100mm×100mm unburnt earth blocks improved with
corncob ash (CCA) geopolymer cement. Blocks produced at
various percentages of 0, 10, 20, 30, 40, and 50% mix of
clay/CCA geopolymer were crushed at 7 and 28 days of curing to
observe their compressive strength. The block samples were
observed to absorb a higher amount of water with an increase in
the percentage content of CCA Alkali-Silica geopolymer. Careful
observations were made on several properties of the samples such
as the presence of cracks/voids and the level of shrinkage after 28
days of curing. Based on the compressive strength result of 5.87
N/mm2 obtained at 28 days, it was recommended that up to 50%
CCA geopolymer content be used to replace cement by weight.

Uncertainty about the remaining service life of the pavement occurs in
developing countries even though the structure is designed with the
planned life. As a result, there is damage to the network and the structure
and function of the road. Uncertainty in volume, traffic growth that
exceeds the plan, and limited funds have a very strong influence on the
shape of the road. This study aimed to determine the condition of the road
surface and road service functions to find out the service life of the
pavement and the relationship between the Present Serviceability Index
value and the International Roughness Index on the flexible pavement.
Secondary data collection such as IRI and traffic volume is used for
modeling and reviewing road service functions. The results show that the
remaining road life will end in year 8, a 2-year decrease from the 10-year
plan. The relationship between PSI and IRI plans has an R2 value of
0.9981, while the actual condition has an R2 of 0.9976.

In the state of Colorado, the Colorado Department of Transportation (CDOT) utilizes their pavement management system (PMS) to manage approximately 9,100 miles of interstate, highways, and low-volume roads. Three types of deterioration models are currently being used in the existing PMS: site-specific, family, and expert opinion curves. These curves are developed using deterministic techniques. In the deterministic technique, the uncertainties of pavement deterioration related to traffic and weather are not considered. Probabilistic models that take into account the uncertainties result in more accurate curves. In this study, probabilistic models using the discrete-time Markov process were developed for five distress indices: transverse, longitudinal, fatigue, rut, and ride indices, as a case study on low-volume roads. Regression techniques were used to develop the deterioration paths using the predicted distribution of indices estimated from the Markov process. Results indicated tha...

With the general lack of equivalence between the many methods and measures by which road roughness is characterized, standardized indices offer the means to achieve a time-stable data base that can be utilized by all. The International Road Roughness Experiment (IRRE) was organized in Brasilia, Brazil, to find a suitable index and to quantify the relationships between different equipment and roughness indices in use. Roughness measurements were made on 49 test sites by diverse types of equipment in common use. The data were analyzed to determine the equivalence between the roughness measures that could be obtained with each type of equipment and whether one common measure was applicable to all. The results from the IRRE showed that a standard roughness index is practical and measurable by most of the equipment in use today, whether of the profilometer or road meter type. As a result of the IRRE, a standard index was selected that is based on the quarter-car analysis method with standard parameter values and a reference speed of 80 km/h. Provided in this paper is the background on the fundamental of roughness characterization that guided the selection of the standard road roughness index.

Pavement condition prediction helps road agencies to schedule maintenance, rehabilitation, and reconstruction, and to allocate limited funds and resources to such activities. Compared to state highways, pavement performance prediction for local roads has received relatively little attention in the literature due to perceptions of low importance, low levels of investment in data collection, poor data quality, and high variation within the data elements. Additionally, local road pavement condition data may suffer from dataset imbalance, often leading to unreliable condition predictions. Hence, this paper introduces a methodology to predict local pavement condition using various single estimator and ensemble machine learning (ML) models along with the adaptive synthetic sampling method. The study develops nine (9) Bayesian-optimised ML models: category boosting (CatBoost), adaptive boosting, decision tree, extra trees, gradient boosting, light gradient-boosting machine, k-nearest neighbour, random forest, and artificial neural network. The ensemble ML and CatBoost were found to exhibit the best model performance, with an average testing accuracy of 0.82, specificity of 0.81, sensitivity of 0.63, and F-measure of 0.61. These results underscore the efficacy of ensemble ML models in pavement condition prediction. The proposed approach can be beneficial to local road agencies in their long-term planning, scheduling, and budgeting.

Reliable and effective data are required in each stage of planning highway, maintenance & various evaluations such as to identify problems, risk factors, priority treatments, formulate strategy, set targets and monitor performance. Current available raw data are not sufficient for the effective evaluation. These data when properly coded and visualized, processed and analyzed in a systematic way could give a better understanding. This project has carried out pavement condition survey creating a data base, evaluating pavement, predicting pavement performance, prioritizing the needs and developing work program to prepare highway pavement condition mapping by use of GIS(Geographic Information System). This output result can be used to satisfy the requirements of a highway network evaluation and thereby enhancing the efficient performance of the pavement of road by effective maintenance.

The treatment and stabilization of soils make it possible to recycle materials on construction sites in preparation for the installation of a final covering or the construction of a road network. In road geotechnics, various materials such as lime, cement, and pozzolans were used as additions to stabilize clay soils. In recent years, bio-polymers and bacteria are of increasing interest to researchers in the field of stabilization and improvement of the physico-mechanical and chemical characteristics of clay soils. Currently, in place of traditional clay treatment techniques and to minimize environmental problems, natural fibrous waste is being used increasingly and spectacularly. Several studies on biotechnological engineering applications already exist, such as the use of vegetation, algae, bacteria, enzymes, and biopolymers. The northern Algerian region where the urban tissue does not stop expanding contains significant potential in terms of wheat straw, which represents a very pr...

The mining industry faces the challenge of incorporating advanced technology to explore new ways of increasing productivity and reducing costs. Our focus is on integrating drone technology to revolutionize mining tasks like inspection, mapping, and surveying. Drones offer a precision advantage over traditional satellite methods. To this end, we have created a dataset consisting of 373 aerial images captured by a DJI Phantom 4 drone, which depict a mining site in the Benslimane region of Western Morocco. These images, with a ground resolution of 2.5 cm per pixel, are the basis of our research. Our study aims to address the challenges posed by traditional mining techniques and to leverage technological innovations to improve segmentation and classification. The proposed approach includes new methodologies, particularly the combination of K-Means clustering and mathematical morphology, to overcome limitations and deliver better segmentation results. Our findings represent a significant step forward in advancing mining operations through the effective use of modern technologies. This is an open access article under the CC BY-SA license.

A first-order reliability program (FORM) was applied to evaluate the potential of Tropical Red Soil (lateritic soil) admixed with waste wood ash (WWA) and compacted with British Standard Light (BSL) energy for use as road construction material. Regression models were developed from laboratory data using Mini-tab R15 software which served as limit state equations for the reliability analysis. Using the developed models for compaction characteristics, and a well-known distributions for the related soil parameters, safety indices were calculated considering compaction characteristics (maximum dry density, MDD and optimum moisture content, OMC) as dependent variables and the soil parameters (content of WWA, content of clay, content of silt, content of sand and specific gravity as independent variables). The results revealed that safety index is influenced by alteration in the soil parameters. Results shows that content of clay, content of silt, content of sand and specific gravity are significantly affected by the change in coefficient of variation (COV) and for that reason must be firmly controlled in lateritic soil-waste wood ash (WWA) mixtures compacted with BSL energy for use in road pavement as sub-base material. It was evident from the safety index that WWA content has little effect as its value almost continued unchanged at all COV values used. Stochastically, the BSL energy used did not yield satisfactory safety index value of 1.0 as recommended by the Nordic Committee on Building Regulation. Hence, higher energy level with better additives having higher potency such as cement, lime or bitumen is suggested to model compaction characteristics of lateritic soil-waste wood ash (WWA) mixtures for road pavement purpose as sub-base material for 10-100% variation in COV.

The effect of rice husk ash admixed with treated sisal fibre on properties of lateritic soil as a road construction material was studied. The natural soil falls under A-7-6(10) classification using the American Association of State Highway and Transportation Officials (AASHTO) classification and CL (Lean Clay) according to Unified Soil Classification System (USCS). Treatment of the soil was done with Rice Husk Ash (RHA) and treated Sisal Fibre (SF) in stepped concentrations of 0, 2, 4, 6, 8% and 0, 0.25, 0.5, 0.75, 1.0% by dry weight of soil, respectively. Preliminary test, compaction test (using British Standard light (BSL) energy) and unconfined compressive strength (UCS) test were carried out. Analysis of variance was carried out using Microsoft Excel Analysis Tool Pak Software Package. Mini-tab R15 Software was used for regression analysis. Laboratory results were used to generate reliability indices using a FORTRAN based first order reliability program. Outcome of the laboratory tests show that MDD decreased from 1.85 mg/m 3 for the natural soil to 1.73 mg/m 3 at 0% RHA/1% SF content. The OMC increased from 18% for the natural soil to 26.5% at 6% RHA/1% SF content. The UCS increased from 100.57 kN/m 2 for the natural soil to 139.38 kN/m 2 at 0% RHA/0.25%SF content by dry weight of soil. The increasing trend continued to 696.63 kN/m 2 at optimum 6% RHA/0.75%SF content by dry weight of soil, which met the regulatory minimum UCS value of 687-1373 kN/m 2 for pavement applications. Reliability indices varied for all the variables considered with MDD and OMC having more significant effect on the UCS. Based on the results obtained, an optimum blend of 6%RHA/0.75% SF content by dry weight of soil is recommended for used as sub-based material for lightly trafficked roads.

The primary goal of pavement maintenance and rehabilitation (M&R) planning is to achieve transportation agencies' objectives within limited budgets. Therefore, numerous optimization models have been developed for scheduling M&R activities at project or network levels; however, their effectiveness is contingent upon pavement condition deterioration and improvement, M&R alternatives, and various climatic, environmental, and regional factors. Moreover, these models should produce comprehensive, efficient, and practical schedules on which highway agencies can rely. This research study introduces a framework and optimization model for large road networks with different types of pavement surfaces, considering various pavement condition indicators and distresses. Employing the genetic algorithm (GA), a single-objective optimization model was created to generate optimal M&R schedules. The reliability of this GA model was tested to ensure the generation of optimal solutions. Furthermore, the model's sensitivity to budget variations was assessed. The developed framework and GA model were used to analyze Interstate 80 sections in Wyoming, USA. The results demonstrate that the GA model consistently achieved the most cost-effective outcomes over a 5-year period, with fewer than 1,000 iterations. Moreover, given the available budget, implementing the generated treatment plan has the potential to preserve road networks in good condition. The findings suggest that optimization models should consider multiple condition indicators and distresses when developing practical schedules for effective pavement maintenance. It was unearthed that funds should be allocated among network components based on their relative conditions. This article could assist policymakers and asset managers in making budgetary decisions and developing M&R strategies for the long-term sustainability of transportation networks.

Current processes for quality control of Hot Mix Asphalt (HMA) are inefficient and place considerable risks on HMA producers and transportation agencies. This project was established to explore possible new ways of gathering real-time quality control information. The two primary goals of real-time testing are: to dramatically shrink the time lag for quality control information, and to improve the reliability of the data. To meet the objectives of this study, a panel of individuals familiar with current and or new QC technologies was assembled to discuss concepts, brainstorm possibilities, and map out a general strategy for evaluation of new methods and technologies.

This research delves into the realm of asphalt technology, exploring the potential of nanoadditives to enhance traditional asphalt binder properties. Focusing on Nano-Titanium Dioxide (NT), Nano-Aluminum Oxide (NA), and Nano-Silica Oxide (NS), this study investigates the effects of incorporating these nanomaterials at varying dosages, ranging from 0% to 8%, on the asphalt binder's performance. This study employs a series of experimental tests, including consistency, storage stability, rotational viscosity, mass loss due to aging, and rheological properties, to assess the impact of nano-additives on asphalt binder characteristics. The findings indicate a substantial improvement in the consistency of the asphalt binder with the addition of nanomaterials, particularly NS, which shows a 41% reduction in penetration at an 8% content and a notable increase in the softening point. The storage stability tests reveal that NS-modified asphalt exhibits superior stability compared to NT and NA, with a significantly lower ΔT increase. Furthermore, the investigation into rotational viscosity suggests that NS, despite increasing the binder's viscosity, does not exceed the AASHTO M320 threshold, ensuring the binder's workability. Aging tests demonstrate that NT, at lower concentrations, acts as an effective anti-aging agent, whereas NA and NS tend to increase the mass loss, impacting thermal stability. This study concludes that while each nanomaterial uniquely influences the asphalt binder's properties, NS stands out in terms of enhancing the high-temperature performance and storage stability. Optimal dosages of 6% for NT and NA and 4% for NS are recommended based on the Overall Desirability analysis. This research bridges the gap between traditional asphalt materials and modern requirements, highlighting the transformative impact of nano-additives in advancing asphalt pavement technology.

Municipalities and transportation departments worldwide are striving to keep road pavement
conditions acceptable, thus enhancing pavement sustainability. Although the pavement condition
index (PCI) is widely used to assess distress conditions, traditional visual surveys used for
PCI estimation can be laborious, expensive, and time-consuming. The international roughness index
(IRI) can be measured more economically and conveniently than PCI; however, it does not directly
indicate the surface condition of the pavement. In this study, a PCI–IRI correlation is proposed for
urban roads located in the New Beni-Suef region, Egypt. For this purpose, a total of 44 km of urban
roads was divided into homogenous sections. A visual distress survey was conducted to measure
PCI considering typical distress patterns. The IRI values for the same sections were measured using
an ultrasonic distance sensor mounted on an automobile. An exponential model was proposed
to capture the relationship between IRI and PCI. With a coefficient of determination of 0.82, the
exponential model seems to outperform reported IRI-PCI correlations. Model validation, along
with a comparison to the existing models, supports its applicability to a wide range of roads. The
proposed model provides a cost-effective means for accurately predicting PCI based on IRI, which is
particularly useful for pavement maintenance management programs on limited budgets.

Each year, millions of dollars are invested on road maintenance and reparation all over the world. In order to minimize costs, one of the main aspects is the early detection of those flaws. Different types of cracks require different types of repairs; therefore, not only a crack detection is required but a crack type classification. Also, the earlier the crack is detected, the cheaper the reparation is. Once the images are captured, several processes are applied in order to extract the main characteristics for emphasizing the cracks (logarithmic transformation, bilateral filter, Canny algorithm, and a morphological filter). After image preprocessing, a decision tree heuristic algorithm is applied to finally classify the image. This work obtained an average of 88% of success detecting cracks and an 80% of success detecting the type of the crack. It could be implemented in a vehicle traveling as fast as 130 kmh or 81 mph.

This study presents the result of experimental investigation on the evaluation of the effects of Cassava Peel Ash (CPA) on the index and structural properties of lateritic soil of A-7-5 (AASHTO Classification) and SP soil (USCS classification) respectively. The study was in two phases, firstly with the addition of CPA only by weight of dry soil and secondly with the addition of 5% cement with the CPA by weight of dry soil. The CPA additions were performed using 0, 2, 4, 6, 8 and 10% increment by weight of dry soil thoroughly mixed. The results of the treated soil showed that the index properties: plastic limit and the maximum dry density (MDD) decreased as the CPA addition increased whereas the liquid limit, plasticity index, Optimum Moisture Content (OMC) and California Bearing Ratio (CBR) increased as the CPA content increased. It was also discovered that the Unconfined Compressive Strength (UCS) increased up to 4% of CPA addition after which was a reduction. This implies that CPA possesses the potential for use in the modification/ stabilization of lateritic soils. Thus the use of CPA in road construction works can be said to have two major advantagesas an effective agricultural waste management and the improvement of road construction soils for better road performance.