Mechanical properties and microstructure of fibre-reinforced clay blended with by-product cementitious materials

Clayey soils endure adverse changes in strength and volume due to seasonal changes in moisture content and temperature. It has been well recognised that high cement content has been successfully employed in improving the mechanical properties of clayey soils for geotechnical infrastructural purposes. However, the environmental setbacks regarding the use of high cement content in soil reinforcement have necessitated the need for a greener soil reinforcement technique by incorporating industrial by-product materials and synthetic fibres with a reduced amount of cement content in soil-cement mixtures. Therefore, this study presents an experimental study to investigate the mechanical performance of polypropylene and glass fibre-reinforced cement-clay mixtures blended with ground granulated blast slag (GGBS), lime and micro silica for different mix compositions and curing conditions. The unconfined compressive strength, linear expansion and microstructural analysis of the reinforced soils have been studied. The results show that an increase in polypropylene and glass fibre contents caused an increase in unconfined compressive strength but brought on the reduction of linear expansion of the investigated clay from 7.92% to 0.2% at fibre content up to 0.8% for cement-clay mixture reinforced with 5% Portland cement (PC). The use of 0.4–0.8% polypropylene and glass fibre contents in reinforcing cement-clay mixture at 5% cement content causes an increase in unconfined compressive strength (UCS) values above the minimum UCS target value according to American Society for Testing and Materials (ASTM) 4609 after 7 and 14 days curing at 20◦ C to 50◦ C temperature. Therefore, this new clean production of fibre-reinforced cement-clay mixture blended with industrial by-product materials has great potential for a wide range of applications in subgrade reinforcement

Effective Planning Strategies: A Key Component for Implementation of Automation in Academic Libraries

Academic library, as a dynamic, vital organ in an organization or institution of higher learning, plays an important role in the academic programmes in selecting, organizing and disseminating of information to enrich the curriculum; it is deeply affected by the information technology revolution.  Therefore, the need for effective planning strategies in meeting the demand of the present society in transforming the library into new information service centre becomes pertinent. The focus and aim of the paper is to provide steps to be adopted in the implementation of automated library services particularly in Nigeria. Strategies are the focus of all planning processes and it incorporates purpose, policies, programmes, actions, decisions, collaborations and resource allocation. The study also focused on automation activities as the core management preparation or analytical process of identifying of objectives, effectiveness/ineffectiveness in the context of implementation. The study recommended among others in its conclusion, that librarians or information managers should see the steps, factors and the planning process as provided in the study as a way to eliminate wastage of resources, other sources of funding of the project have also been proffered. Keyword: Automation, Planning, Strategies, Library, and Components

Localization and Mapping for Self-Driving Vehicles:A Survey

The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicles’ localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains

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

Strength Predictive Modelling of Soils Treated with Calcium-Based Additives Blended with Eco-Friendly Pozzolans—A Machine Learning Approach

Abstract: The unconfined compressive strength (UCS) of a stabilised soil is a major mechanical parameter in understanding and developing geomechanical models, and it can be estimated directly by either lab testing of retrieved core samples or remoulded samples. However, due to the effort, high cost and time associated with these methods, there is a need to develop a new technique for predicting UCS values in real time. An artificial intelligence paradigm of machine learning (ML) using the gradient boosting (GB) technique is applied in this study to model the unconfined compressive strength of soils stabilised by cementitious additive-enriched agro-based pozzolans. Both ML regression and multinomial classification of the UCS of the stabilised mix are investigated. Rigorous sensitivity-driven diagnostic testing is also performed to validate and provide an understanding of the intricacies of the decisions made by the algorithm. Results indicate that the well-tuned and optimised GB algorithm has a very high capacity to distinguish between positive and negative UCS categories (‘firm’, ‘very stiff’ and ‘hard’). An overall accuracy of 0.920, weighted recall rates and precision scores of 0.920 and 0.938, respectively, were produced by the GB model. Multiclass prediction in this regard shows that only 12.5% of misclassified instances was achieved. When applied to a regression problem, a coefficient of determination of approximately 0.900 and a mean error of about 0.335 were obtained, thus lending further credence to the high performance of the GB algorithm used. Finally, among the eight input features utilised as independent variables, the additives seemed to exhibit the strongest influence on the ML predictive modelling

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

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

Modified orange peel waste as a sustainable material for the adsorption of contaminants

The world orange production is estimated as 60 million tons per annum, while the annual production of orange peel wastes is 32 million tons. According to available data, the adsorption capacity of orange peel ranges from 3mg/g to 5mg/g while their water uptake is lower than 1mg/g, the low water uptake of orange peel and abundance of the biomass in nature has made orange peel excellent biosorption material. This review summarised different studies on orange peel adsorption of various contaminants to identify properties of orange peel that influence the adsorption of contaminants. Most of the literatures reviewed studied orange peel adsorption of heavy metals, followed by studies on adsorption of dyes, while few literatures investigated adsorption of oil by orange peel. FTIR spectra analysis and SEM micrographs of raw and activated orange peels were studied to understand structural properties of the biomass responsible for adsorption. The study identified pectin, hydroxyl, carbonyl, carboxyl, and amine groups as component and important functional groups responsible for adsorption in orange peel. Furthermore, changes were observed in the structural properties of the peel after undergoing various forms of modifications. Physical modification increased the surface area for binding and adsorption of contaminants, while chemical treatments increased the carboxylic groups enhancing adsorption and binding of contaminants. In addition, heating orange peel during thermal modification process resulted in highly porous structure and subsequent increase in adsorption capacities. In conclusion, physical, chemical, and thermal treatments improve structural properties of orange peel, resulting in high biosorption uptake. However, orange peels treated with chemicals recorded the highest contaminants adsorption capacities

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

Viability of calcinated wastepaper sludge ash geopolymer in the treatment of road pavement subgrade materials

Problematic ground conditions constituted by weak or expansive clays are commonly encountered in construction projects and require some form of chemical treatment such as lime and cement to re-engineer their performance. However, in the light of the adverse effects of these traditional additives on the climate, alternative eco-friendlier materials are now sourced. In the current study, the viability of calcinated wastepaper sludge ash geopolymer in enhancing the engineering behaviour of a problematic site condition is evaluated. A highly expansive clay (HEC) constituted with a blend of kaolinite and bentonite clays is treated with calcinated wastepaper sludge ash (CPSA) geopolymer. Activation of the precursor is actualised at room temperature using a combination of NaOH and Na2SiO3 at various activator to soil+binder ratios (AL/P), and molarity (M). The mechanical, microstructural, and mineralogical characteristics of the treated clay were investigated through unconfined compressive strength (UCS), swell, water absorption, SEM, and EDX analysis. The performance of the stabilised samples was then compared with the requirements for road subgrade and subbase materials and that of OPC and lime-GGBS treatment. The results showed that CPSA-geopolymer enhanced the engineering properties of the treated clay better than traditional binders (OPC and Iime-GGBS). UCS improvement of 220% was observed in the CPSA-stabilised soil over that of OPC-treated ones, while the swell potential and water absorption were drastically reduced by over 95 and 97% respectively after 28-day soaking. The SEM and EDX results showed improved crystallisation of earth-metal-based cementitious flakes (NASH) with increasing CPSA, molarity, and AL/P ratios, which enhanced the inter-particle bonds with simultaneous reduction in porosity. The modified characteristics of the stabilised materials meet the requirements for pavement subgrades. Further, the equivalent carbon emission (CO2-e) from the stabilised materials were also evaluated and compared with that of traditional binders. The results also showed that CPSA-geopolymer had lower CO2-e at higher subgrade strengths than OPC, making it more eco-friendly. Therefore, wastepaper sludge, a common landfill waste from paper recycling is a viable geopolymer precursor that could be utilised in enhancing the engineering properties of subgrade and sub-base materials for road and foundation construction