Real Field Applications Research Articles

Automated vision-based concrete crack measurement system

Maintaining and ensuring the safety of ageing infrastructures is becoming of uttermost importance. This is particularly critical in Europe, where several infrastructures like tunnels and bridges are approaching their end-of-life. Such a condition justifies the need for a prompt recognition of the deterioration status of these structures as well as the identification of strategies for effective inspection and monitoring operations. This paper focuses on cracks, and proposes a vision-based measurement system aimed at precisely and accurately detecting cracks on concrete surfaces and measuring their geometric features. The system mainly leverages RGB-D cameras to make the pixel to real world measurement units conversion possible (by assessing the camera to target relative distance and pose), and exploits an image processing pipeline consisting of the following steps: (a) an artificial intelligence-based semantic segmentation model for identifying cracks on the target structure; (b) a ridge detection algorithm for identifying the centre line of the cracks; (c) a geometric feature extraction algorithm to extract key metrics of the crack, like its width and length. Variance analysis of the crack mean width measurement shows a repeatability standard deviation of 0.01 mm and a reproducibility standard deviation of 0.02 mm, with a Type A expanded uncertainty of 0.004 mm. The system performance is discussed in the paper referring both to lab testing activities, targeted to the assessment of its metrological characteristics, and to a real field application, i.e. concrete inspection in a tunnel. This latter application is of particular interests as it is targeted to demonstrate the robustness of the approach in harsh environments (i.e., strong presence of dust, total absence of natural light) and on surfaces whose roughness generates important light gradients and shadows (e.g., sprayed concrete).

Bright yellow fluorescent carbon dots as selective nanoprobe for detection of 2-nitrophenol and 4-nitrophenol in aqueous medium

Different types of phenolic pollutants pose a major risk to the environment and the health of living things. It is therefore crucial for their selective recognition especially nitrophenols. In this work, yellow fluorescent carbon dots (C-dots) were used as selective nanoprobe to efficiently detect 2-nitrophenol (2-NP) and 4-nitrophenol (4-NP). One-pot synthetic method was adopted for making C-dots from o-phenylenediamine and oleic acid mixture. The synthesized C-dots showed an excitation wavelength of 420 nm with Stokes shift of up to 135 nm. These C-dots exhibited instantaneous selective detection of 2-NP and 4-NP in aqueous solution with a detection limit of 6.4 nM and 4.8 nM respectively. Upon incubated with 2-NP and 4-NP, the C-dots’ fluorescence underwent quenching with a maximum extent of respective 80 % and 83 %. By comparing with other analogs of analyte, it was discovered that the emission intensity of the C-dots remained unchanged. Static quenching was proposed behind the changes of fluorescence after the treatment with 2-NP and 4-NP. Such type of C-dots will demonstrate the selective sensing platform in real field application.

Assessing the physicochemical properties of surface/groundwater of Sudhnoti district, Azad Jammu and Kashmir: Impacts on lindane degradation by photocatalysis

Since polluted water deteriorates the health of human beings and other animals, healthy society and environmental sustainability demand for clean and safe water. The presence of contaminants, and imbalance of common physicochemical parameters significantly disturb the quality of water. Taking the importance of clean and safe water into account, this study investigated fourteen physicochemical parameters of hand pump and open well water (main drinking water sources) from fifteen places of district Sudhnoti, Azad Jammu and Kashmir (AJK). The mean values of pH, electrical conductivity (EC), alkalinity, hardness and total dissolved solids (TDS) were 7.6, 349.9 μS/cm, 144, 152 and 190 mg/L, respectively. The mean concentration of Na+, K+, Ca2+, Mg2+, HCO3−, Cl−, SO42− and NO3− was found to be 9.5, 0.96, 9.9, 44.2, 144, 16.8, 8.1 and 6.67 mg/L, respectively. The turbidity as well as the concentration of CO32− was found below the detectable level in all water samples tested. Interestingly, all the studied physicochemical parameters had lower values than the maximum permissible level recommended by the world health organization (WHO) for drinking water. Eventually, the impacts of the studied physicochemical parameters, particularly anions and cation, on the efficiency of water treatment technology for real field application were discussed. The results revealed that physicochemical parameters of the groundwater of Sudhnoti, AJK had a slight effect on the degradation efficiency of one of the most widely used pesticides, lindane, in water by using ultraviolet–visible titanium oxide/hydrogen peroxide (UV–Vis)/TiO2/H2O2) and ultraviolet–visible/titanium oxide persulfate photocatalysis. Of note, the quality of hand pump and open well water which are the main drinking water sources of district Sudhnoti, AJK was found to be fit for drinking and other domestic purposes as well as for the ecosystem.

Electrospun rGO-PVDF/WO3 composite fibers for SO2 sensing

Developing highly selective and sensitive SO2 sensors is essential due to the excessive presence of SO2 in the environment which can cause respiratory illness and acid rain. Semiconducting metal oxides are the materials most used for the development of SO2 sensors; however, they still suffer from sensitivity, selectivity, low-temperature operation, low detection limit, and slow response/recovery time for real-field applications. In this work, rGO-PVDF/WO3 composite fibre-based sensors are developed via electrospinning technique. The electrospun rGO-PVDF/WO3 were characterized for their structural morphology, thermal stability, crystal structure, and their SO2 sensing capability. The results show that the electrospun rGO-PVDF/WO3 sensor (containing 10 mL GO) leads the most improved SO2 sensing performance of all tested sensors in this work, with a response of 11.08 and 22.06 at SO2 concentrations of 60 and 80 ppm, respectively. The 10 mL-rGO-PDVF/WO3 composite nanofibers also demonstrated good selectivity and stability for SO2. The improvement in gas sensitivity is due to the synergistic effect of forming an ohmic contact between the rGO nanosheets, PVDF fibers, and the WO3 nanograins. The good surface properties such as high surface area (10.64 m2/g) and porous structure of 10 mL-rGO-PDVF/WO3 composite nanofibers have also contributed to the SO2 gas sensing performance. The results show that the composite nanofibers rGO-PVDF/WO3 can be used to detect low concentrations of SO2 and could be used to monitor the environment.

Completion Performance Evaluation in Multilateral Wells Incorporating Single and Multiple Types of Flow Control Devices Using Grey Wolf Optimizer

There has been a tendency in oil and gas industry towards the adoption of multilateral wells (MLWs) with completions that incorporate multiple types of flow control devices (FCDs). In this completion technique, passive inflow control devices (ICDs) or autonomous inflow control devices (AICDs) are positioned within the laterals, while interval control valves (ICVs) are installed at lateral junctions to regulate the overall flow from each lateral. While the outcomes observed in real field applications appear promising, the efficacy of this specific downhole completion combination has yet to undergo comparative testing against alternative completion methods that employ a singular flow control device type. Additionally, the design and current evaluations of such completions are predominantly based on analytical tools that overlook dynamic reservoir behavior, long-term production impacts, and the correlation effects among different devices. In this study, we explore the potential of integrating various types of flow control devices within multilateral wells, employing dynamic optimization process using numerical reservoir simulator while the Grey Wolf Optimizer (GWO) is used as optimization algorithm. The Egg benchmark reservoir model is utilized and developed with two dual-lateral wells. These wells serve as the foundation for implementing and testing 22 distinct completion cases considering single-type and multiple types of flow control devices under reactive and proactive management strategies. This comprehensive investigation aims to shed light on the advantages and limitations of these innovative completion methods in optimizing well and reservoir performance. Our findings revealed that the incorporation of multiple types of FCDs in multilateral well completions significantly enhance well performance and can surpass single-type completions including ICDs or AICDs. However, this enhancement depends on the type of the device implemented inside the lateral and the control strategy that is used to control the ICVs at the lateral junctions. The best performance of multiple-type FCD-based completion was achieved through combining AICDs with reactive ICVs which achieved around 75 million USD profit. This represents 42% and 22% increase in the objective function compared to single-type ICDs and AICDs installations, respectively. The optimal settings for ICD and AICD in individual applications may significantly differ from the optimal settings when combined with ICVs. This highlights a strong correlation between the different devices (control variables), proving that using either a common, simplified analytical, or a standard sequential optimization approach that do not explore this inter-dependence between devices would result in sub-optimal solutions in such completion cases. Notably, the ICV-based completion, where only ICVs are installed with lateral completion, demonstrated superior performance, particularly when ICVs are reactively controlled, resulting in an impressive 80 million USD NPV which represents 53% and 30% increase in the objective function compared to single-type ICDs and AICDs installations, respectively.

Luminescent metal–organic frameworks for ultrasensitive detection of pesticide and biomarker: In situ imaging and real field application

Frequent uses of pesticides for agricultural led to the environmental pollution and ultimately threaten the human body. On the other hand, the monitoring of the biomarker at the optimum level is also very imperative for human health. So the investigation of smart materials is very essential for visual and on‐site monitoring of pesticide and biomarker. In this regard, we have synthesized highly fluorescent MOF (ZnATA, 1) oriented with free‐NH2 functionality as a guest functional moiety. Framework 1 showed the selective and sensitive ratiometric detection of biomarker riboflavin (RBF). Further, sensor 1 exhibited the discriminating recognition of pesticide 2,6‐dichloro‐4‐nitroaniline (DCNA). The detection limit for both RBF and DCNA is very excellent, and the values are 0.13 and 0.07 ppm, respectively. The detection of RBF and DCNA is very fast (within 3 s) and has been realized through colorimetric response and by cost‐effective paper strip method. Interestingly, the sensor displayed the visual and on‐site (in situ imaging by using real fruit and vegetable) recognition of “DCNA” within 3 s. Sensor 1 further demonstrated the recognition of DCNA in different real field sample (environmental water sample) and RBF in food samples (milk and vitamin B2 tablet). The mechanism of sensing for DCNA and RBF has been comprehended through the occurrence of photo‐induced electron transfer, forster resonance energy transfer, and inert filter effect. Further, the recognition mechanism is justified by the theoretical study. The presence of free‐NH2 moiety in the linker of MOF is possibly to establish a strong supramolecular interaction with the analytes.

Resistance against tree encroachment is driven by richness and identity of herbaceous resident species

AbstractAimsHerbaceous plant cover can be used to inhibit tree encroachment in many managed and semi‐natural grasslands. Ideally, herbaceous seed mixtures should reduce the establishment and growth of many invading tree species over several years. Theory predicts that invasion resistance can be maximized by both: (a) increasing the diversity (taxonomic and/or functional) of resident seed mixtures, and (b) including species that are especially efficient at blocking further colonizers. We established an experiment in a regularly mown old field within a deciduous forest region in southeastern Canada. Our overall goal was to examine the relative contribution of old field community composition and diversity (taxonomic and functional) to resistance against encroachment by two undesirable native tree species over time.LocationVarennes, southern Québec, Canada (45°37′01.24″ N, 73°23′03.57″ W).MethodsWe tested our hypotheses by seeding experimental plots with different levels of taxonomic diversity (from one to nine species) and functional group richness (grasses, legumes, and forbs). Our herbaceous species comprised four native species and five introduced and naturalized species. We then seeded the plots with two model tree colonizers, Acer rubrum and Betula populifolia, during three consecutive years. We also transplanted seedlings of Acer and Betula to determine the effect of herbaceous diversity on post‐emergence growth and seedling survival.ResultsOverall, herbaceous species identity (presence and absence) better predicted the resistance of resident herbaceous seed mixtures to tree encroachment than the other diversity measures assessed in our study. Fast‐growing herbaceous species, such as Lolium, substantially inhibit resistance to woody encroachment in the first year. Achillea millefolium and Solidago canadensis were by far the most efficient in blocking tree seedling establishment and growth, particularly during the second and third years. These effects highlight the potential for a combination of fast‐growing species, along with slower‐growing but highly inhibitive species, as a way to limit tree encroachment in real field applications. Functional group richness was a poor predictor of tree encroachment. Species richness reduced tree encroachment, especially when considering a multidimensional tree encroachment index integrating all aspects of tree establishment and growth over the three years of our experiment.ConclusionsSeeding highly inhibiting herbaceous species is the most efficient approach against specific tree invaders. However, it is difficult to implement in practice because it relies on a priori knowledge of the species’ inhibitory effects. In the absence of this knowledge, the taxonomic richness of seed mixtures should be increased to prevent tree encroachment.

Dual-functional porphyrinic zirconium-based metal-organic framework for the fluorescent sensing of histidine enantiomers and Hg2.

A novel fluorescence sensor based on a porphyrinic zirconium-based metal-organic framework, L-cysteine-modified PCN-222 (L-Cys/PCN-222), was developed to selectively recognize histidine enantiomers and sensitively detect Hg2+. The dual-functional sensor was successfully prepared via the solvent-assisted ligand incorporation method and characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), 1H nuclear magnetic resonance (1H NMR) spectroscopy, Fourier transform infrared (FT-IR) spectroscopy, circular dichroism (CD), X-ray photoelectron spectroscopy (XPS), and nitrogen adsorption-desorption analyses. L-Cys/PCN-222 not only showed a higher quenching response for L-histidine than that for D-histidine with a fast fluorescent response rate of <40 s but also exhibited low detection limits for L- and D-histidine (2.48 μmol L-1 and 3.85 μmol L-1, respectively). Moreover, L-Cys/PCN-222 was employed as a fluorescent and visual sensor for the highly sensitive detection of Hg2+ in the linear range of 10-500 μmol L-1, and the detection limit was calculated to be 2.79 μmol L-1 in surface water. The specific and selective recognition of chiral compounds and metal ions by our probe make it suitable for real field applications.

Development of Underground Perimeter Intrusion Detection System

An underground passive security system based on minimal electric charge detection was developed and implemented in the real application field. The fixed length of the sensor cable is buried underground named a security zone. It is generated an alarm when an intruder goes into the security zone. A shielded telecommunication cable can be used for sensing the generated minimal charges by the external intrusion. If two different types of materials are rubbed with each other, minimal electric charges are generated, called the triboelectric effect. Minimal electric charges are generated between the cable shield and conductors when forces and impacts are applied to the underground buried cable. The control device detects and amplifies generated minimal charges due to the intruders on the sensor cable. The proposed system can be shown a reliable intrusion detection rate of up to 96%-97% when we have kept and considered all installation and environmental conditions.

Strategic utilization of microalgae byproducts for sustainable biofertilization of Camellia sinensis (green tea)

Green algae are well known for their plant growth-promoting role, but the biofertilization potential of some industrial algae waste has yet to be tested. Industrial algae production facilities are increasing rapidly yearly, but most growing liquids are unused and remain as waste. Here we examined the effects of various microalgae byproducts as tea plant growth biostimulants. The roles of cell-free supernatant (CFS), sonicated microalgae biomass and living cells of the freshwater alga Chlorella vulgaris in promoting tea plant growth were investigated through real field applications. All three algae by-products of the same culture were applied to the tea plants three times either from aerial parts or from the soil. After 40 days of incubation, it was determined that all microalgae waste, even CFS, significantly improved the vegetative growth of the treated tea plants. CFS were also able to improve chlorophyll accumulation, node length, leaf length, and leaf width by 23%–26%, 62%–69%, 5%–36%, and 23%–54%, respectively. Notably, the sonicated microalgae effluents were able to improve the node growth by 123%. To our knowledge, this is the first study revealing the significant biofertilization potential of C. vulgaris CFS for shoot growth of tea plants. The obtained results highlight the agricultural potentials of both microalgae culture broths and degreased microalgae wastes as sustainable and environmentally-friendly biofertilizers for Camellia sinensis organic farming.

Operando Li metal plating diagnostics via MHz band electromagnetics

A nondestructive detection method for internal Li-metal plating in lithium-ion batteries is essential to improve their lifetime. Here, we demonstrate a direct Li-metal detection technology that focuses on electromagnetic behaviour. Through an interdisciplinary approach combining the ionic behaviour of electrochemical reactions at the negative electrode and the electromagnetic behaviour of electrons based on Maxwell’s equations, we find that internal Li-metal plating can be detected by the decrease in real part of the impedance at high-frequency. This finding enables simpler diagnostics when compared to data-driven analysis because we can correlate a direct response from the electronic behaviour to the metallic material property rather changes in the ionic behaviour. We test this response using commercial Li-ion batteries subject to extremely fast charging conditions to induce Li-metal plating. From this, we develop a battery sensor that detects and monitors the cycle-by-cycle growth of Li-metal plating. This work not only contributes to advancing future Li-ion battery development but may also serve as a tool for Li-metal plating monitoring in real-field applications to increase the useable lifetime of Li-ion batteries and to prevent detrimental Li-metal plating.

Scale-Up Soybean Crude Urease Calcite Precipitation (SCU-CP) Method for Sandy Soil Improvement

Loose sandy soils tend to have low shear strength; thus, improving the mechanical properties is necessary to enhance the bond of soil grains. Adding an additive that will form a bonding agent, i.e., calcite, within the soil to increase the strength of the soil is a relatively novel technique. Soybean Crude Urease Calcite Precipitation (SCU-CP) is a soil improvement method that utilizes soybeans as a catalyst for calcite formation in the reaction of urea and CaCl2. This study aims to evaluate the efficacy of SCU-CP in the large-scale application in terms of the distribution of calcite and its impact on the behavior of the California Bearing Ratio. A soil sample made with a 1-m acrylic mold with a 5cm diameter and a drum test with a height and diameters of 50 cm and 60 cm, respectively. The results in a percentage distribution of material vertically on a 1-m acrylic mold and horizontally on a 60 cm diameter drum with an even distribution. The average percent spread is 3.96% in the mold and 7.19% in the drum-test even distribution of strength, where the average CBR result is 1.40 on the seventh day. This study concluded that the SCU-CP method with the pouring technique has a promising approach for real-field application.

Efficacious degradation of ethylene glycol in baffled ozonation reactor in the presence of waste-derived MIL-53(Al/Fe)-metal-organic framework derived Al2O3/Fe3O4

Ethylene glycol (EG) has been widely used to produce polyethylene terephthalate (PET) bottles, antifreezes, and de-icing fluid. However, EG possesses toxic detrimental effects on human and aquatic creatures. The degradation of EG in catalytic ozonation based advanced oxidation processes was rarely reported before. In this regard, a novel porous catalyst, magnetic MIL-53(Al/Fe)-metal-organic framework (MOF) derived Al2O3/Fe3O4 (wAl-Fe@MOF) was synthesized for the first-time using PET, iron, and aluminum scraps for efficacious catalytic ozonation system (COS) of EG. A maximal EG degradation and mineralization of 97.03 ± 2.79% and 57.75 ± 2.60% were obtained in wAl-Fe-based COS, which was higher than sole O3 (70.76 ± 3.90%; 22.65 ± 2.20%) and O3/H2O2 (74.51 ± 2.58%; 36.57 ± 3.10%). The wAl-Fe@MOF displayed only about 18% drop in deterioration efficiency after 5th cycle of reusability, demonstrating outstanding stability and recyclability. The electron paramagnetic resonance and radical scavenging tests confirmed that surface adsorbed ˙OHads were majorly accountable for EG degradation. Phytotoxicity revealed that COS effectively reduced the toxicity of EG towards Vigna radiata. The energy consumption and operating cost were found to be 0.863 kWh m−3 order−1 and 0.086 $ m−3 order−1, indicating wAl-Fe@MOF based COS could be highly appropriate for wastewater purification in real field applications.

Internet of agriculture: Analyzing and predicting tractor ride comfort through supervised machine learning

The aim of this study is to improve ride comfort among tractor drivers by utilizing Internet of Things (IoT) technology and Machine Learning (ML) techniques to analyze and predict vibration exposure. The study explores the association between tractor ride characteristics, including average speed, tool depth, and pulling force, and ride comfort during actual rotary soil tillage operations. Ride comfort was evaluated by calculating the Overall Vibration Value (OVV) at three measurement locations (i.e., the floor, seat pan, and seat backrest). The study employed spectral analysis i.e., Fast Fourier Transform (FFT) and Power Spectral Density (PSD) to determine the predominant resonant frequencies. In addition, ML approaches i.e., Linear Regression (LR), Decision Tree Regressor (DTR), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Artificial Neural Network (ANN) were used to predict ride comfort response. Three hyperparameter optimization techniques, including Grid Search, Random Search, and Bayesian optimization were used. Tractor-tiller system was operated on a real agricultural field by five male tractor drivers. Vibration levels were measured along three translational axes at three measurement locations using a smart monitoring device based on the IoT concept. Average OVV magnitude was computed as 0.84 m/s2 i.e., exceeding the ISO 2631-1 (1997) threshold limits. FFT and PSD analysis revealed a variety of dominant frequencies, including resonance peaks at 2 Hz, 6–7 Hz, and 9–11 Hz. The analysis of variance (ANOVA) indicates that tractor speed and tool depth are significant variables (at the 5% level) in determining ride comfort. Regarding predictive accuracy, the ANN model exhibits the highest coefficient of determination of 0.90, followed by the SVM and GPR models with a coefficient of determination of 0.89. The DTR and LR models demonstrate slightly lower coefficient of determination values of 0.83 and 0.82, respectively. Furthermore, Bayesian optimization proved to be an effective approach in achieving more accurate predictions of tractor ride comfort. In general, the study outcomes can be utilized by tractor designers in numerous ways to optimize tractor design (such as suspension system, cab design, seat design, etc.) to improve tractor ride comfort in real field applications.

Adsorption of harmful dyes and antimicrobial studies utilizing recyclable ZnO, its composites with conventionally used activated carbon, and waste orange peel as a greener approach

In this work, zinc oxide (ZnO) nanoparticles and its composites with activated carbon (AC) and orange peel (OP) were synthesized, characterized through scientific techniques such as FESEM, EDX, HRTEM, BET, FTIR, XRD etc, and used as adsorbents to treat methylene blue (MB) and methyl orange (MO) in wastewater. Different process parameters (like change in pH value, amount of adsorbent, concentration of dye, temperature, and contact time) were considered to examine the dye removal efficiency of ZnO and its composites. Maximum uptake capacity for MB/MO removal obtained was found to be 175.7/110.68 mg/g (ZnO), 180.03/107.97 mg/g (ZnO@AC) and 205.38/166.25 mg/g (ZnO@OP). Furthermore, thermodynamic, kinetic, and isothermic variables were calculated to get a clear vision about the mechanism involved during adsorption of both the dyes. The reusability examination of all adsorbents suggested continued reuse of adsorbents even after five cycles. Considering true adsorption performance, partition coefficient values were calculated for all adsorbents, e.g., 37.86/13, 41.1/12.45, and 73.61/32.48 mg.g-1.μM-1 in case of ZnO, ZnO@AC, and ZnO@OP for removal of MB/MO dyes, respectively. Furthermore, the zone of inhibition was estimated by performing antimicrobial experiments against Bacillus subtilis (ZnO@OP 19 mm ˃ ZnO & ZnO@AC 14 mm), Staphylococcus aureus (ZnO@OP 18 mm ˃ ZnO 17 mm ˃ ZnO@AC 12 mm), Pseudomonas aeruginosa (ZnO@OP 20 mm ˃ ZnO 16 mm ˃ ZnO@AC 13 mm), and Escherichia coli (ZnO@OP 19 mm ˃ ZnO 15 mm ˃ ZnO@AC 12 mm) respectively. Also, real water studies confirmed their promising future as adsorbents for real field applications.

Proposal of a methodology for mass optimization of realistic steel structural systems composed of columns and galleries for support of solid bulk conveyors

Unlike most works in the literature that propose the optimization of benchmark problems or very simple real structures, with idealized loads and restrictions, this work proposes a complete methodology to optimize real support structures of belt conveyor lines, considering realistic loads and restrictions imposed by international technical codes. Thus, the present work proposes a comprehensive methodology to minimize the mass of realistic steel structural systems composed of columns and galleries to support solid bulk conveyors. The objective is to obtain the minimum mass for different interactions between gallery spans and column heights for conveyors, taking into account the optimization of size, shape, and topology of each structural subsystem (columns and galleries) and thus obtaining the mass per unit of length of the conveyor line. For this, recurrent steel support structures are identified, analyzed, and selected according to their performance and, structurally resisting the applied load inputs, they are designed to minimize the structural system mass. The Enhanced Particle Swarm Optimization (EPSO) algorithm is used considering multiple constraints involving stress, displacement, buckling, frequency, and acceleration limits according to various international codes. An example of a realistic conveyor line is presented, in which 42 different interactions among the dimensions of the subsystems are simulated and the minimum mass of the system is obtained for each one of them. Thus, the results obtained allow, from the knowledge of the geometrical characteristics of the span and height of the system, to identify the optimized linear mass for any situation within the study boundary, resulting in an important practical contribution. Additionally, the graphs presented allow the designer to choose optimized solutions in terms of cost and raw material consumption for real-field applications. It was possible to determine that appropriate choices represent up to 35% mass reduction of structural systems.

Design of flexible pavements through fuzzy inference system with genetic algorithm optimized rule base

In this paper, a novel method for the design of flexible pavements is elaborated. The method is based on fuzzy inference system with genetic algorithm (GA) aided optimized rule base. The model is founded on layered fuzzy antecedent and consequent conjunctive rules. The data for the model consists of 300 flexible pavement design instances that breaks up in to 25% of the data drawn from research and real field applications and 75% of data generated in spread sheets compliant with Indian road congress (IRC) code guidelines. In the first step, the inputs and outputs were fuzzified and around 110 rules were generated using training data set. GA was implemented to find optimal and a compact rule set. GA was able to garner 35 rules that are adequate to predict the thickness of base course, sub base and surface course with high accuracy. The model with optimized rules was validated using test data set. The results of the evaluation are encouraging with low values of RMSE ranging between 3.6–11 for GSB, binder course (BC) and surface course (SC). The coefficient of determination is also high and between 0.85–0.90 indicating accuracy in prediction. Correlation coefficient values stood at an average of 0.92 indicating closeness between predicted and actual values of thickness of courses.

Gecko-inspired self-adhesive packaging for strain-free temperature sensing based on optical fibre Bragg gratings

The large development of fibre Bragg gratings (FBGs) over decades has made this kind of structures one of the most mature optical fibre sensing technologies existing today, demonstrating key features for a very wide range of applications. FBG sensors are fragile and must be normally protected for real-field applications, although challenging packaging designs are required to mitigate temperature-strain cross-sensitivity issues. Here, a polydimethylsiloxane (PDMS) packaging with a microarray structure that provides gecko-inspired dry adhesion is proposed for strain-free FBG-based temperature sensing. Besides offering protection, the PDMS packaging with an embedded polyamide capillary damps the mechanical strain transferred to the optical fibre, providing FBG-based temperature sensing with a negligible impact of strain. In addition, the microarray structure imprinted on one surface of the packaging provides gecko-inspired dry adhesion based on van der Waals forces. This feature enables the packaged optical fibre sensor to be attached and detached dynamically to nearly any kind of smooth surface, leaving no residuals in the monitored structure. Experimental results verify a fast and accurate temperature response of the sensor with highly mitigated impact of residual strain. The proposed packaged sensor can be used in application where glue is not allowed nor recommendable to be used.

Hybrid intelligence models for compressive strength prediction of MPC composites and parametric analysis with SHAP algorithm

Nowadays, hybrid soft computing technics are attracting the scholars of construction materials field due to their high adaptability and prediction performances to data information. Hence, the current research aims to predict the compressive strength of magnesium phosphate cement (MPC) composites using the deep learning and machine learning based hybrid models, which is rarely seen in the literature. Data was collected from published papers, where 70% data used for training the models and 30% for testing stage. Four different hybrid models like CNN-LSTM, CNN-GRU, DTR-RFR and GBR-RFR were formulated to achieve the goals by comparing their forecasting performances with statistical parameters. Additionally, governing input variable parameters and prediction process explanation were also interpreted by SHAP algorithm under hybrid models. As is observed, all selected hybrid models presented the good corroboration to output CS data with higher accuracy results. Besides, CNN-LSTM and GBR-RFR models exhibited the superior fitness (R2 ≈ 0.99) to strength properties in relation to other three models at both phases. Average error ranges were observed very condense to ± 5%. Moreover, testing age was observed as the most influential variable to model outputs. Furthermore, it was exposed that CNN-LSTM model can well interpret the interactions of inputs to outputs and inner-working process of prediction, whereas GBR-RFR describes the dependence plot at decent level to elucidate the connections among the inputs for model outputs. However, the proposed hybrid approaches of the research might be a potential solution to optimize the mix design of MPC mixtures containing supplementary cementitious materials (SCMs) and well predict the strength characteristics of MPC matrices for real field applications by engineering practitioners.

1D and 2D Field Effect Transistors in Gas Sensing: A Comprehensive Review.

Rapid progress in the synthesis and fundamental understanding of 1D and 2D materials have solicited the incorporation of these nanomaterials into sensor architectures, especially field effect transistors (FETs), for the monitoring of gas and vapor in environmental, food quality, and healthcare applications. Yet, several challenges have remained unaddressed toward the fabrication of 1D and 2D FET gas sensors for real-field applications, which are related to properties, synthesis, and integration of 1D and 2D materials into the transistor architecture. This review paper encompasses the whole assortment of 1D-i.e., metal oxide semiconductors (MOXs), silicon nanowires (SiNWs), carbon nanotubes (CNTs)-and 2D-i.e., graphene, transition metal dichalcogenides (TMD), phosphorene-materials used in FET gas sensors, critically dissecting how the material synthesis, surface functionalization, and transistor fabrication impact on electrical versus sensing properties of these devices. Eventually, pros and cons of 1D and 2D FETs for gas and vapor sensing applications are discussed, pointing out weakness and highlighting future directions.