Research highlights? The use of multiple regression (MR), artificial neural network (ANN) and artificial neuro-fuzzy inference system (ANFIS) models, for the prediction of swell percent of soils, was described and compared. ? However the accuracies of ANN and ANFIS models may be evaluated relatively similar, it is shown that the constructed ANN models of RBF and MLP exhibit a high performance than ANFIS and multiple regression for predicting swell percent of clays. ? The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. ? The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations. In the recent years, new techniques such as; artificial neural networks and fuzzy inference systems were employed for developing of the predictive models to estimate the needed parameters. Soft computing techniques are now being used as alternate statistical tool. Determination of swell potential of soil is difficult, expensive, time consuming and involves destructive tests. In this paper, use of MLP and RBF functions of ANN (artificial neural networks), ANFIS (adaptive neuro-fuzzy inference system) for prediction of S% (swell percent) of soil was described, and compared with the traditional statistical model of MR (multiple regression). However the accuracies of ANN and ANFIS models may be evaluated relatively similar. It was found that the constructed RBF exhibited a high performance than MLP, ANFIS and MR for predicting S%. The performance comparison showed that the soft computing system is a good tool for minimizing the uncertainties in the soil engineering projects. The use of soft computing will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations.

Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils

Earth fissure hazard prediction using machine learning models.

The reduction in the ability of bitumen to bond with the aggregate surface due to the infiltration of moisture has been recognised for years, and this deterioration phenomenon is called moisture damage. In general, the loss of bonding between bitumen and aggregate shortens the service life of the top layer of the pavement. Many investigations have been conducted to understand the mechanisms of moisture damage due to the loss of bonding strength between bitumen and aggregate and to find ways to improve and strengthen the bond to mitigate the effect of moisture. This paper reviews the extensive literature on the loss of bitumen-aggregate bonding strength due to moisture damage in asphalt mixtures. The general description of the theories and mechanisms that explain the effect of the thermodynamic, chemical, physical and mechanical characteristics of the bitumen and aggregate on the bonding phenomenon are discussed in this paper. In addition, the causes of and contributing factors to moisture damage and methods to improve the bond between bitumen and aggregates are also discussed. Moreover, a description of the test methods that can be used to evaluate moisture damage in poorly bonded and compacted mixtures are also presented. Special attention is given to a well-known method, known as the pull-off test, which has been successfully used to evaluate aggregate-binder bond strength, both for laboratory and in-situ tests. This includes the test methods, the factors that affect the bonding strength results and their correlation with other test method. A review of the failure mode of bitumen under the pull-off loading test is discussed in the final section of this paper.

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1282&context=ccee_pubs

Effects of moisture damage on asphalt mixtures

Mineral filler, fine-grained mineral particles naturally present in or manufactured and added to aggregates play a significant role on the performance of asphalt mastic and asphalt mixtures. A clea...

Role of mineral filler in asphalt mixture

Nano-scale moisture damage evaluation of carbon nanotube-modified asphalt

Palm oil is one of the leading agricultural crops in the world, as it dominates 34% of the global vegetable oil market, with approximately 64.6*103 million kgs of production in 2017. However, along with its breakthrough, the generation of palm oil mill effluent (POME) as uncontrolled waste has become a serious matter and requires proper management to reduce its negative effects on the environment. Subsequently, the high organic content of POME makes it possible to convert waste into value-added products, such as biogas. A ratio of 0.5 for biological oxygen demand to chemical oxygen demand (BOD/COD) indicates a high possibility for biological treatment. Recently, the utilisation of POME as a cheap source for biogas production has gained an extraordinary amount of attention, and intensive research has been conducted on the upstream to downstream process. Finding the most suitable and efficient pretreatment technique and reactor configuration are vital parameters for the treatment and conversion of POME to biogas. This review describes existing pretreatment processes for POME and recommends recently manufactured high-rate anaerobic reactors as the most suitable and efficient pretreatment technique for maximising the extraction of biogas from POME.

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Recent advances on palm oil mill effluent (POME) pretreatment and anaerobic reactor for sustainable biogas production

The sign language, considered as the main language for deaf and hard of hearing, uses manual communication and body language to convey expressions and plays a major role in developing an identity. Nowadays, sign language recognition is an emerging field of research to improve interaction with the deaf community. The automatic recognition of American, British, and French sign languages with high accuracy has been reported in the literature. Even though, Bangla is one of the mostly spoken languages in the world, no significant research on Bangla sign language recognition can be found in the literature. The main reason for this lagging might be due to the unavailability of a Bangla sign language dataset. In this study, we have presented a large dataset of Bangla sign language consisting of both alphabets and numerals. The dataset was composed of 7052 samples representing 10 numerals and 23864 samples correspond to the 35 basic characters of the alphabet. Finally, the performance of a convolutional neural network in the recognition of numerals and alphabet separately, and in mixing of them, has been evaluated on the developed dataset using 10-fold cross-validation. The proposed method provided an average recognition accuracy of 99.83%, 100%, and 99.80%, respectively for basic characters, numerals, and for their combined usage.

Recognition Bangla Sign Language using Convolutional Neural Network

Damage due to moisture in polymer modified asphalt pavements has been investigated for several decades; yet, the exact and mathematical causes of moisture are not precisely known. Nanoscale experiment has been conducted in this study with an atomic force microscopy (AFM) to determine these effects in terms of adhesive and cohesive forces. A base asphalt binder and one polymer styrene---butadiene---styrene (SBS) were utilized to modify asphalt binders, which was used to prepare sample for testing on glass substrates under AFM. The asphalt samples were conditioned under wet and dry conditions. Current study formulates an artificial intelligence rule which predicts the moisture damage relation in lime and SBS modified asphalts. Base asphalt binders have shown larger adhesion/cohesion values compared to the polymer modified asphalt samples under dry conditions. However, this trend is opposite under wet conditions. Base binders are more susceptible to moisture damage than the polymer modified asphalt binders. ANFIS model (as compared to MLP and SVM) was found to be very promising in these points. The mean relative error was very low 0.02 and 0.03, respectively, for projected and observed data, which also showed the steady performance of the model. Statistical analysis was also performed for dry sample by executing of the three neural network models and found MLP's performance was very good to other two models.

Moisture damage evaluation in SBS and lime modified asphalt using AFM and artificial intelligence

The modification by polymers and nanomaterials can significantly improve different properties of asphalt. However, during the service life, the oxidation affects the constituents of modified asphalt and subsequently results in deviation from the desired properties. One of the important properties affected due to oxidation is the adhesive properties of modified asphalt. In this study, the adhesive properties of asphalt modified with the polymers (styrene-butadiene-styrene and styrene-butadiene) and carbon nanotubes were investigated. Asphalt samples were aged in the laboratory by simulating the field conditions, and then adhesive properties were evaluated by different tips of atomic force microscopy (AFM) following the existing functional group in asphalt. Finally, a predictive modelling and machine learning technique called the classification and regression tree (CART) was used to predict the adhesive properties of modified asphalt subjected to oxidation. The parameters that affect the behaviour of asphalt have been used to predict the results using the CART. The results obtained from CART analysis were also compared with those from the regression model. It was observed that the CART analysis shows more explanatory relationships between different variables. The model can predict accurately the adhesive properties of modified asphalts considering the real field oxidation and chemistry of asphalt at a nanoscale.

/pdf/modelling-of-asphalt-s-adhesive-behaviour-using-8n2oun2lgt.pdf

Modelling of Asphalt's Adhesive Behaviour Using Classification and Regression Tree (CART) Analysis.

OF DISSERTATION Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

/pdf/nano-scale-evaluation-of-moisture-damage-in-asphalt-57cktwqdcg.pdf

Arifuzzaman

Papers

Recent advances on palm oil mill effluent (POME) pretreatment and anaerobic reactor for sustainable biogas production

Recognition Bangla Sign Language using Convolutional Neural Network

Moisture damage evaluation in SBS and lime modified asphalt using AFM and artificial intelligence

Modelling of Asphalt's Adhesive Behaviour Using Classification and Regression Tree (CART) Analysis.

Nano-scale evaluation of moisture damage in asphalt