Prediction models for sewer infrastructure utilizing rule-based simulation

Management of infrastructure projects is becoming increasingly challenging due to inherent uncertainties. The most effeective way to deal with uncertainty is to collect supplementary information and knowledge. When expensive or infeasible, quantification of uncertainty may be performed using analytical or simulation techniques. The City of Edmonton, Canada has approximately 4600 km of sewer pipes in the combined, sanitary, and storm sewer local systems with uncertainty issues related to deterioration. The City has taken a proactive approach with respect to sewer rehabilitation, as it is more cost-effeective to repair a defective pipe prior to failure rather than after a collapse. This article demonstrates an approach for predicting the condition of a sewer pipe and the related cost of rehabilitation, given the limited data. Three models are described in this article that are developed to assist the City of Edmonton to effeectively plan maintenance expenditure. Each model uses a combination of rule-based simulation and probability analysis to assist in the planning of future expenditures for sewer maintenance, thereby producing an invaluable planning tool.     

Improving outdoor air quality based on building morphology: Numerical investigation

Due to rapid urbanization around the world, high concentrations of vehicular pollutants have deteriorated the outdoor air quality, which can affect the physical and psychological well-being of humans. Numerous strategies have been proposed to overcome these harmful impacts by improving the dispersion of air pollutants. Consequently, a question arises regarding the potential effects of building morphology on the dispersion of pollutants. Subsequently, transient three-dimensional Computational Fluid Dynamics (CFD) simulations are performed to examine the effect of building morphology on PM₁₀ dispersion. Eleven cases with various prototypes and morphological methods are compared with a simple building form to identify the patterns of PM₁₀ dispersion within a given time sequence under a prevailing inflow condition. The results indicate that the different designs of building morphology with varying Relative compactness (R_C) indicator highlight the importance of considering morphological factors to improve outdoor air quality. In addition, the proposed prototypes can reduce PM₁₀ concentrations by approximately 30%–90% at specific points in the studied time sequence. In particular, the vertical, horizontal, and grid folded prototypes can be considered more effective as an approximate decrease between 70% and 90% in PM₁₀ concentrations is observed, which reflects the influence of building morphology on improving outdoor air quality.     

Progressive collapse of cold-formed steel framed structures

This study investigated the possibility of progressive collapse of a cold-formed steel framed structure. Five different analysis cases were considered, exterior wall column removal specified in General Services Administration (GSA) guidelines and Department of Defense (DoD) guidelines, corner wall column removal specified in GSA and DoD guidelines, and analysis by Successive Removal. The results showed that the removal of corner wall columns appeared to cause progressive collapse of a portion of the second and third floor of the end bay directly associated with the column removal, and not the entire building.     

Simulation of low cycle fatigue stress‐strain response in 316LN stainless steel using non‐linear isotropic kinematic hardening model—A comparison of different approaches

Cyclic stress‐strain response of 316LN stainless steel subjected to low cycle fatigue at strain amplitude of ±0.4% and at 873 K is simulated using finite element analysis with non‐linear isotropic‐kinematic hardening Chaboche model. Four different approaches have been used in simulating cyclic stress response and hysteresis loops: 3 based on Chaboche model‐parameters and the fourth on direct experimental data (stabilized loop and cyclic stress‐strain curve [CSSC]). Among them, simulations performed with direct experimental data have not yielded expected initial cyclic response. The source of data used for evaluation of kinematic‐hardening (KH) parameters determined the extent of closeness between experimental results and Chaboche‐model predictions. KH parameters determined from first‐cycle loop and modified‐CSSC predicted the overall stress‐strain response (from initial to stabilized condition) with reasonable fit, compared with other approaches. All 4 approaches though predicted stabilized response, simulations based on “KH‐parameters from stabilized‐cycle” accurately described stabilized response with coefficient of determination (r²) 0.995.     

A study of neutron and gamma radiation effects on transmission of various types of glasses, optical coatings, cemented optics and fiber

A study of radiation effects on various types of glasses, dielectric optical coatings, cemented optics and fiber was undertaken with a view to select them for extreme radiation environments. Samples were exposed to different radiation doses in the Pakistan Research Reactor-I (PARR-I) for neutron and Cobalt 60 source for gamma irradiation. Transmissions were measured before and after irradiation. The dielectric coatings were subjected to additional tests (adhesion, abrasion and humidity, etc.) as per MIL-M-13508C and MIL-C-675C. All 15 glasses studied showed varying amounts of transmission loss as expected, with negligible degradation for three types. Recovery of transmissions with time/ageing was also studied, with more or less complete recovery with temperature annealing. A faster bleaching of darkened/brown glasses was achieved by using UV lamps or UV laser. The dielectric coatings (HR, AR) and one of the two commercial optical cements showed excellent resistance to neutrons and gamma radiations, and could be good candidates for the fabrication and utilization of optical components in extreme radiation environments. The data allowed several Chinese glasses to be studied for the first time.     

Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs

In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error?=?3.362 and root mean square error?=?0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron–artificial neural network (MLP-ANN) indicates that the GA/SVD-ANFIS method has superior performance in simulating the discharge coefficient of side weirs.     

Carbon nanotubes play an important role in the spatial arrangement of calcium deposits in hydrogels for bone regeneration

Age related bone diseases such as osteoporosis are considered among the main causes of reduced bone mechanical stability and bone fractures. In order to restore both biological and mechanical function of diseased/fractured bones, novel bioactive scaffolds that mimic the bone structure are constantly under development in tissue engineering applications. Among the possible candidates, chitosan-based thermosensitive hydrogel scaffolds represent ideal systems due to their biocompatibility, biodegradability, enhanced antibacterial properties, promotion of osteoblast formation and ease of injection, which makes them suitable for less invasive surgical procedures. As a main drawback, these chitosan systems present poor mechanical performance that could not support load-bearing applications. In order to produce more mechanically-competent biomaterials, the combined addition of hydroxyapatite and carbon nanotubes (CNTs) is proposed in this study. Specifically, the aim of this work is to develop thermosensitive chitosan hydrogels containing stabilised single-walled and multi-walled CNTs, where their effect on the mechanical/physiochemical properties, calcium deposition patterns and ability to provide a platform for the controlled release of protein drugs was investigated. It was found that the addition of CNTs had a significant effect on the sol–gel transition time and significantly increased the resistance to compression for the hydrogels. Moreover, in vitro calcification studies revealed that CNTs played a major role in the spatial arrangements of newly formed calcium deposits in the composite materials studied, suggesting that they may have a role in the way the repair of fragile and/or fractured bones occurs in vivo.     

Effect of bioactive dental adhesive on periodontal and endodontic pathogens

The objectives of this study were to: (1) develop a new bioactive dental bonding agent with nanoparticles of amorphous calcium phosphate and dimethylaminohexadecyl methacrylate for tooth root caries restorations and endodontic applications, and (2) investigate biofilm inhibition by the bioactive bonding agent against eight species of periodontal and endodontic pathogens for the first time. Bonding agent was formulated with 5?% of dimethylaminohexadecyl methacrylate. Nanoparticles of amorphous calcium phosphate at 30?wt% was mixed into adhesive. Eight species of biofilms were grown on resins: Porphyromonas gingivalis, Prevotella intermedia, Prevotella nigrescens, Aggregatibacter actinomycetemcomitans, Fusobacterium nucleatum, Parvimonas micra, Enterococcus faecalis, Enterococcus faecium. Colony-forming units, live/dead assay, biomass, metabolic activity and polysaccharide of biofilms were determined. The results showed that adding dimethylaminohexadecyl methacrylate and nanoparticles of amorphous calcium phosphate into bonding agent did not decrease dentin bond strength (P?>?0.1). Adding dimethylaminohexadecyl methacrylate reduced the colony-forming units of all eight species of biofilms by nearly three orders of magnitude. The killing efficacy of dimethylaminohexadecyl methacrylate resin was: P. gingivalis?>?A. actinomycetemcomitans?>?P. intermedia?>?P. nigrescens?>?F. nucleatum?>?P. micra?>?E. faecalis?>?E. faecium. Dimethylaminohexadecyl methacrylate resin had much less biomass, metabolic activity and polysaccharide of biofilms than those without dimethylaminohexadecyl methacrylate (P?<?0.05). In conclusion, a novel dental adhesive was developed for root caries and endodontic applications, showing potent inhibition of biofilms of eight species of periodontal and endodontic pathogens, and reducing colony-forming units by three orders of magnitude. The bioactive adhesive is promising for tooth root restorations to provide subgingival margins with anti-periodontal pathogen capabilities, and for endodontic sealer applications to combat endodontic biofilms.     

Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and non-seizure. In addition, a fast Walsh-Hadamard Transform (FWHT) technique is implemented to analyze the EEG signals within the recurrence space of the brain. Thus, Hadamard coefficients of the EEG signals are obtained via the FWHT. Moreover, the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings. Also, a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers. The LSTM classifier provides the best performance, with a testing accuracy of 99.00%. The training and testing loss rates for the LSTM are 0.0029 and 0.0602, respectively, while the weighted average precision, recall, and F1-score for the LSTM are 99.00%. The results of the SVM classifier in terms of accuracy, sensitivity, and specificity reached 91%, 93.52%, and 91.3%, respectively. The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s, respectively. The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals. Eventually, the proposed classifiers provide high classification accuracy compared to previously published classifiers.     

Financial assessment of health and safety programmes in the workplace

In evaluating health and safety improvements for performance improvement, it is necessary to account for both the contributions of a healthy workforce and the resources required supporting it.

The Economic Assessment of the Work Environment (EAWE) is a financial framework that helps management forecast the financial benefits of health and safety implementations. The five-step process comprises (1) a health assessment to identify critical elements in the work environment, (2) an action plan to address gaps, (3) performance targets based on internal goals and external benchmarks, (4) transformation of the expected improvements in health and safety into expected performance outcomes, and (5) implementation in stages, starting from individual jobs to entire organisation.

EAWE offers a dynamic framework for corporate decision-makers when evaluating health and safety programmes. Further research should explore the bounds of EAWE across different types of organisations and the evolution of performance over time.