Incorporation of a nanotechnology-based additive in cementitious products for clay stabilisation

The mechanical performances and water retention characteristics of clays, stabilised by partial substitution of cement with by-products and inclusion of a nanotechnology-based additive called RoadCem (RC), are studied in this research. The unconfined compression tests and one-dimensional oedometer swelling were performed after 7 d of curing to understand the influence of addition of 1% of RC material in the stabilised soils with the cement partially replaced by 49%, 59% and 69% of ground granulated blast furnace slag (GBBS) or pulverised fuel ash (PFA). The moisture retention capacity of the stabilised clays was also explored using the soil-water retention curve (SWRC) from the measured suctions. Results confirmed an obvious effect of the use of RC with the obtained strength and swell properties of the stabilised clays suitable for road application at 50% replacement of cement. This outcome is associated with the in-depth and penetrating hydration of the cementitious materials by the RC and water which results in the production of needle-like matrix with interlocking filaments – a phenomenon referred to as the ‘wrapping’ effect. On the other hand, the SWRC used to describe the water holding capacity and corresponding swell mechanism of clays stabilised by a proportion of RC showed a satisfactory response. The moisture retention of the RC-modified clays was initially higher but reduced subsequently as the saturation level increased with decreasing suction. This phenomenon confirmed that clays stabilised by including the RC are water-proof in nature, thus ensuring reduced porosity and suction even at reduced water content. Overall, the stabilised clays with the combination of cement, GGBS and RC showed a better performance compared to those with the PFA included

Data on one-dimensional vertical free swelling potential of soils and related soil properties

Most of the damaging geo-hazards recorded in modern history are caused by soil swelling or expansion. Therefore, proper evaluation of a soil's capacity to swell is very crucial for the achievement of a secure and safe ground for civil infrastructures and related land developments which are founded on the soil. In order to simulate as well as estimate the heave that can occur under field conditions, laboratory one-dimensional oedometer vertical swell-strain testing are most frequently used. Hence, in this brief, one-dimensional swelling tests adopted to measure soil swelling on laboratory-engineered and natural soils covering various regions on the globe are reported. The testing standards and procedures followed in the measurement of one-dimensional swelling are those enumerated in the American Standards for Testing of Materials (ASTM), and American Association of State Highways Transport Officials (AASHTO). Slight modifications to the measurement procedures (such as the use of different surcharge loading and custom-made consolidation rings) reflecting special laboratory testing conditions and for the purposes of comparisons, are also reported.Corresponding soil properties characterising the dataset includes moisture content, void ratio, specific gravity, unit weight, liquid limit, plastic limit, plasticity index, clay content, silt content, maximum dry unit weight, optimum moisture content, and soil activity index, all of which are known to bear either direct or indirect influences on soil. Determination of the state of compaction of the soils where applicable, are carried out based on the American Standards for Testing of Materials (ASTM), Turkish Standards (TS), American Association of State Highways Transport Officials (AASHTO)and a combination of both standard and modified efforts. A total of 395 data samples on soil swelling potential are reported. With regards to the corresponding soil properties, a total of 219 data records of soil specific gravity, 321 data records of initial moisture content, 163 data records of void ratio, 273 data records of dry unit weight, 347 data records of liquid limit, 347 data records of plastic limit, 395 data records of plasticity index, 209 data records of activity index, 339 data records of clay content, 174 data records of silt content, 246 data records of optimum moisture content, 228 data records of maximum dry density and 347 data records of Unified Soil Classification System (USCS) are presented. Finally, the dataset of one-dimensional soil swelling described herein are intended to aid geotechnical engineers and researchers who are involved in statistical correlation studies, data analytics, and machine learning predictions using soft computing methods mostly aimed at evaluating soil expansion especially during the preliminary phases of soil investigation and foundation design

An Analysis of the Extent of Implementation of Environmental Cost Management and Its Impact on Output of Oil and Gas Companies in Nigeria, (2001-2010)

This study was set out to critically analyzed the extent of implementation of environmental cost management and its impact on output of oil and gas companies in Nigeria from 2001 to 2010. The paper was aim at ascertaining the extent to which implantation of environment cost management has impacted on the oil and gas industries in Nigeria. Using multiple regression analytical technique," data from the central bank of Nigeria (CBN) and Environmental Impact Assessment Agency were obtained. Findings revealed that there exist a significant relationship between the parameters that influence environmental cost management and output of oil and gas produced in Nigeria. Also, it was discovered that there are no established standards in Nigeria guiding environmental cost management in the oil and gas industries in Nigeria. Again there is a lacuna in external reporting of environmental cost data in Nigeria. It was concluded that the extent of environmental cost management in the oil and gas industries is at  its rudimentary stage. It was however recommended inter alia that; there should be improvement in external reporting of environmental cost data in the oil and gas industries in Nigeria. And the adoption  of the United Nations Environmental cost Management Accounting (ECMA) guidelines which will enhance the formulation of a Generally Accepted Accounting Principles (GAAP) in Nigeria, which will evolve environmental cost management accounting practice. This will facilitate the global campaign for environmentally enhanced society.   Keywords: Social contract, Eco-efficiency, Environmental quality cost, Environment pollution prevention costs,                          Environmental internal failure costs, Environmental external failure costs, Environmental detection cost

Improved prediction of clay soil expansion using machine learning algorithms and meta-heuristic dichotomous ensemble classifiers

Soil swelling-related disaster is considered as one of the most devastating geo-hazards in modern history. Hence, proper determination of a soil's ability to expand is very vital for achieving a secure and safe ground for infrastructures. Accordingly, this study has provided a novel and intelligent approach that enables an improved estimation of swelling by using kernelised machines (Bayesian linear regression (BLR) & bayes point machine (BPM) support vector machine (SVM) and deep-support vector machine (D-SVM)); (multiple linear regressor (REG), logistic regressor (LR) and artificial neural network (ANN)), tree-based algorithms such as decision forest (RDF) & boosted trees (BDT). Also, and for the first time, meta-heuristic classifiers incorporating the techniques of voting (VE) and stacking (SE) were utilised. Different independent scenarios of explanatory features’ combination that influence soil behaviour in swelling were investigated. Preliminary results indicated BLR as possessing the highest amount of deviation from the predictor variable (the actual swell-strain). REG and BLR performed slightly better than ANN while the meta-heuristic learners (VE and SE) produced the best overall performance (greatest R2 value of 0.94 and RMSE of 0.06% exhibited by VE). CEC, plasticity index and moisture content were the features considered to have the highest level of importance. Kernelized binary classifiers (SVM, D-SVM and BPM) gave better accuracy (average accuracy and recall rate of 0.93 and 0.60) compared to ANN, LR and RDF. Sensitivity-driven diagnostic test indicated that the meta-heuristic models’ best performance occurred when ML training was conducted using k-fold validation technique. Finally, it is recommended that the concepts developed herein be deployed during the preliminary phases of a geotechnical or geological site characterisation by using the best performing meta-heuristic models via their background coding resource

Sonographic Correlation of Liver Dimension and Anthropometric Variables of Height, Weight and Body Mass Index (BMI)

Background: Estimation of liver size can be used as an index to monitor various aspects of liver disease and response to therapy.Purpose: To evaluate the relationship between anthropometric variables (Height, Weight and Body Mass Index) with liver size was carried out in subjects with clinically and sonographically confirmed normal liver.Materials & Methods: This prospective sonographic study was carried out in Calabar, Uyo, Zaria and Makurdicosmopolitan cities of Nigeria. Scans were performed on 388 subjects and their liver sizes measured in the Midclavicular and anterior axillary lines, respectively. Patients’ heights and weights were also measured and used to calculate their respective body mass indices.Results: Mean liver diameter in the study population was 12.9±1.7cm (Range 9.2 – 15.2cm) and 11.6±1.7cm (Range 8.0 – 14.5cm) at the midclavicular and anterior axillary lines respectively. About 98.5 % of the study population had liver sizes ≤15.0cm while 1.5% had sizes at the upper limits of 15.3 – 16cm. Height and BMI appeared to have sone influence on liver size (r=+60; P<0.05, +0.65; P<0.05) respectively at the midclavicular line but not at the anterior axillary line. An insignificant relationship was observed with weight and liver size (r= +0.1; P<0.05) both in the MCL and AAL.Conclusion: Liver size is affected more by individual’s height and body mass index and less by their weight in the region studied

Endoparasites of Bucks Raised under Intensive and Semi-Intensive System

An investigation was carried out on bucks in intensive and semi-intensive systems of management. A total of sixteen (16) bucks (male goats) were randomly purchased for the study. The animals were divided into four groups of four animals per treatment and fed Panicum maximum, Gliricidia sepium for Treatment 1 while Treatment 2 were fed Panicum maximum, Gliricidia sepium plus concentrates. Those in T3 were fed concentrate and allowed to graze and T4 were fed Panicum maximum and were also allowed to forage. The result showed significant difference (P<0.05) in infestations of strongyles amongst the treatments

Machine learning regression and classification algorithms utilised for strength prediction of OPC/by-product materials improved soils

In this study, stand-alone machine (ML) models (Bayesian regressor (BLR), least square linear regressor (REG), artificial neural networks (ANN), and logistic regression (LR)), tree-ensemble ML models (boosted decision tree (BDT), random decision forest (RDF) decision jungle (DJ)) and meta-ensemble ML models (voting (VE) and stacking (SE)) are applied to predict the strength of different soils improved by part-substitution of OPC with PFA and GGBS in various combinations and proportions. Multiclass elements of these proposed ML models are also deployed to provide analysis across multiple cross-validation methods. Results of regression analysis indicated higher statistical variance of OPC-substituted predictor variables compared to soils improved by OPC alone when using both stand-alone and tree-based algorithms. On average, the REG model produced strength predictions with higher accuracy (RMSE of 0.39 and R of 0.86) compared to ANN (RMSE of 0.44 and R of 0.82), but with comparatively lower accuracy compared to tree-based models (average RMSE of 0.33 and R of 0.90) and meta-ensemble models (average RMSE of 0.06 and R of 0.91). For ML classification, multiclass neural network algorithm (mANN) produced higher accuracy (0.78), precision (0.67) and rate of recall (0.67) compared to tree-based models but fell short to meta-ensemble models (average accuracy of 0.80, precision of 0.70 and recall of 0.71). Diagnostic tests across different validation methods indicated better performance of the VE model compared to its SE ML counterpart when adopting the train-validation split technique. Overall, the ensemble methods were more versatile on regression and multiclass classification problems because they aggregated multiple learners to provide robust predictions

Inclusion of RoadCem additive in cementitious materials for soil stabilization

The use of sustainable binders in soil stabilization has dominated most discussions in geotechnical engineering in recent years. In keeping with the objective of reducing carbon foot-printing, a zeolite/alkali earth metal-based additive called “RoadCem” (RC) was used with granulated blast furnace slag (GGBS) in a partially substituted cement-stabilized soil in this research. RoadCem is an additive that is manufactured based on nanotechnology and comprises synthetic zeolite, alkaline metals, and some complex activators as some of its constituents. A unique combination of the binders with up to 50% of cement replaced in a stabilized soil was carried out. Results of an extensive laboratory study (unconfined compressive strength and consolidation swelling tests) indicated an improvement of the mechanical properties of the stabilized soil. An obvious effect of RC was observed in the short-term unconfined compressive strength gain under 7 days with 50% of cement replaced. A considerable reduction in the swelling (up to about 0%) was also noticed in the stabilized soil containing GGBS and RC compared to the soil stabilized by using cement alone. When used to stabilize sulphate-rich soil, results indicated reduced heave by approximately 75% when 1% of RC was added to the cementitious mix with 50% of cement substituted