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16 pages, 4785 KiB

Open AccessArticle

Modeling the Impact of Liquid Polymers on Concrete Stability in Terms of a Slump and Compressive Strength

by Ahmed Salih Mohammed, Wael Emad, Warzer Sarwar Qadir, Rawaz Kurda, Kawan Ghafor and Raed Kadhim Faris

Appl. Sci. 2023, 13(2), 1208; https://doi.org/10.3390/app13021208 - 16 Jan 2023

Cited by 10 | Viewed by 1674

Abstract

It is generally known that the two most crucial elements of concrete that depend on the slump value of the mixture are workability and compressive strength. In addition, slump retention is more delicate than the commonly used slump value since it reflects the [...] Read more.

It is generally known that the two most crucial elements of concrete that depend on the slump value of the mixture are workability and compressive strength. In addition, slump retention is more delicate than the commonly used slump value since it reflects the concrete mixture’s durability for usage in civil engineering applications. In this study, the effect of three water-reducer additives was tested on concrete’s workability and compressive strength from 1 day to 28 days of curing. The slump of the concrete was measured at the time of adding water to the mix and after 30 min of adding water. This study employed 0–1.5% (%wt) water-reducer additives. The original ratio between water and cement (wc) was 0.65, 0.6, and 0.56 for mixtures incorporating 300, 350, and 400 kg of cement. It was lowered to 0.3 by adding water-reducer additives based on the additives type and cement content. Depending on the kind and amount of water-reducer additives, w/c, gravel content, sand content, crushed content, and curing age, adding water-reducer additives to the concrete increased its compressive strength by 8% to 186%. When polymers were added to the concrete, they formed a fiber net (netting) that reduced the space between the cement particles. As a result, joining the cement particles quickly enhanced the fresh concrete’s viscosity and the hardened concrete’s compressive strength. The study aims to establish mathematical models (nonlinear and M5P models) to predict the concrete compressive strength when containing water-reducer additives for construction projects without theoretical restrictions and investigate the impact of mix proportion on concrete compressive strength. A total of 483 concrete samples modified with 3 water-reducer additives were examined, evaluated, and modeled for this study. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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21 pages, 3750 KiB

Open AccessArticle

Three Steps towards Better Forecasting for Streamflow Deep Learning

by Woon Yang Tan, Sai Hin Lai, Fang Yenn Teo, Danial Jahed Armaghani, Kumar Pavitra and Ahmed El-Shafie

Appl. Sci. 2022, 12(24), 12567; https://doi.org/10.3390/app122412567 - 08 Dec 2022

Cited by 6 | Viewed by 1299

Abstract

Elevating the accuracy of streamflow forecasting has always been a challenge. This paper proposes a three-step artificial intelligence model improvement for streamflow forecasting. Step 1 uses long short-term memory (LSTM), an improvement on the conventional artificial neural network (ANN). Step 2 performs multi-step [...] Read more.

Elevating the accuracy of streamflow forecasting has always been a challenge. This paper proposes a three-step artificial intelligence model improvement for streamflow forecasting. Step 1 uses long short-term memory (LSTM), an improvement on the conventional artificial neural network (ANN). Step 2 performs multi-step ahead forecasting while establishing the rates of change as a new approach. Step 3 further improves the accuracy through three different kinds of optimization algorithms. The Stormwater and Road Tunnel project in Kuala Lumpur is the study area. Historical rainfall data of 14 years at 11 telemetry stations are obtained to forecast the flow at the confluence located next to the control center. Step 1 reveals that LSTM is a better model than ANN with R 0.9055, MSE 17,8532, MAE 1.4365, NSE 0.8190 and RMSE 5.3695. Step 2 unveils the rates of change model that outperforms the rest with R = 0.9545, MSE = 8.9746, MAE = 0.5434, NSE = 0.9090 and RMSE = 2.9958. Finally, Stage 3 is a further improvement with R = 0.9757, MSE = 4.7187, MAE = 0.4672, NSE = 0.9514 and RMSE = 2.1723 for the bat-LSTM hybrid algorithm. This study shows that the

δ Q

model has consistently yielded promising results while the metaheuristic algorithms are able to yield additional improvement to the model’s results. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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19 pages, 6042 KiB

Open AccessArticle

Technical Energy Assessment and Sizing of a Second Life Battery Energy Storage System for a Residential Building Equipped with EV Charging Station

by Farhad Salek, Shahaboddin Resalati, Denise Morrey, Paul Henshall and Aydin Azizi

Appl. Sci. 2022, 12(21), 11103; https://doi.org/10.3390/app122111103 - 02 Nov 2022

Cited by 5 | Viewed by 1873

Abstract

This study investigates the design and sizing of the second life battery energy storage system applied to a residential building with an EV charging station. Lithium-ion batteries have an approximate remaining capacity of 75–80% when disposed from Electric Vehicles (EV). Given the increasing [...] Read more.

This study investigates the design and sizing of the second life battery energy storage system applied to a residential building with an EV charging station. Lithium-ion batteries have an approximate remaining capacity of 75–80% when disposed from Electric Vehicles (EV). Given the increasing demand of EVs, aligned with global net zero targets, and their associated environmental impacts, the service life of these batteries, could be prolonged with their adoption in less demanding second life applications. In this study, a technical assessment of an electric storage system based on second life batteries from electric vehicles (EVs) is conducted for a residential building in the UK, including an EV charging station. The technical and energy performance of the system is evaluated, considering different scenarios and assuming that the EV charging load demand is added to the off-grid photovoltaic (PV) system equipped with energy storage. Furthermore, the Nissan Leaf second life batteries are used as the energy storage system in this study. The proposed off-grid solar driven energy system is modelled and simulated using MATLAB Simulink. The system is simulated on a mid-winter day with minimum solar irradiance and maximum energy demand, as the worst case scenario. A switch for the PV system has been introduced to control the overcharging of the second life battery pack. The results demonstrate that adding the EV charging load to the off-grid system increased the instability of the system. This, however, could be rectified by connecting additional battery packs (with a capacity of 5.850 kWh for each pack) to the system, assuming that increasing the PV installation area is not possible due to physical limitations on site. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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17 pages, 5864 KiB

Open AccessArticle

Electrification of a Class 8 Heavy-Duty Truck Considering Battery Pack Sizing and Cargo Capacity

by Aiden Thomas Leonard, Farhad Salek, Aydin Azizi and Shahaboddin Resalati

Appl. Sci. 2022, 12(19), 9683; https://doi.org/10.3390/app12199683 - 27 Sep 2022

Cited by 5 | Viewed by 3330

Abstract

The design and performance optimization of fully electric trucks constitute an integral goal of the transport sector to meet climate emergency measures and local air quality requirements. Most studies in the literature have determined the optimum pack size based on economic factors, without [...] Read more.

The design and performance optimization of fully electric trucks constitute an integral goal of the transport sector to meet climate emergency measures and local air quality requirements. Most studies in the literature have determined the optimum pack size based on economic factors, without accounting for the details of pack behavior when varying the size. In this paper, the effect of battery pack sizing and cargo capacity of a class 8, 41-ton truck on its overall energy performance and technical parameters of its powertrain is investigated. For this purpose, the proposed electric truck is designed and mathematically modelled using AVL CRUISE M software. The second-order equivalent circuit model is developed to predict the battery packs’ parameters. The proposed battery pack model is extracted from experimental analysis on SONY VTC6 lithium-ion batteries performed in the lab. The weight changes due to adding the battery packs to the truck are also estimated and have been taken into account. The mathematical model of the powertrain is simulated in the long-haul driving cycle considering different cargo capacities and battery pack sizes. The results of this study revealed that the battery pack voltage reached its minimum value when the maximum cargo capacity was applied for the 399 kWh battery pack. In addition, increasing the occupied cargo capacity from 10% to 100% resulted in an increase in the regenerative brake energy of up to 9.87 kWh, while changing the battery size imposed minimal impacts on regenerative brake energy recovery as well as energy consumption. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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28 pages, 6720 KiB

Open AccessArticle

Understanding and Predicting the Usage of Shared Electric Scooter Services on University Campuses

by Seyed Mohammad Hossein Moosavi, Zhenliang Ma, Danial Jahed Armaghani, Mahdi Aghaabbasi, Mogana Darshini Ganggayah, Yuen Choon Wah and Dmitrii Vladimirovich Ulrikh

Appl. Sci. 2022, 12(18), 9392; https://doi.org/10.3390/app12189392 - 19 Sep 2022

Cited by 6 | Viewed by 3346

Abstract

Electric vehicles (EVs) have been progressing rapidly in urban transport systems given their potential in reducing emissions and energy consumptions. The Shared Free-Floating Electric Scooter (SFFES) is an emerging EV publicized to address the first-/last-mile problem in travel. It also offers alternatives for [...] Read more.

Electric vehicles (EVs) have been progressing rapidly in urban transport systems given their potential in reducing emissions and energy consumptions. The Shared Free-Floating Electric Scooter (SFFES) is an emerging EV publicized to address the first-/last-mile problem in travel. It also offers alternatives for short-distance journeys using cars or ride-hailing services. However, very few SFFES studies have been carried out in developing countries and for university populations. Currently, many universities are facing an increased number of short-distance private car travels on campus. The study is designed to explore the attitudes and perceptions of students and staff towards SFFES usage on campus and the corresponding influencing factors. Three machine learning models were used to predict SFFES usage. Eleven important factors for using SFFESs on campus were identified via the supervised and unsupervised feature selection techniques, with the top three factors being daily travel mode, road features (e.g., green spaces) and age. The random forest model showed the highest accuracy in predicting the usage frequency of SFFESs (93.5%) using the selected 11 variables. A simulation-based optimization analysis was further conducted to discover the characterization of SFFES users, barriers/benefits of using SFFESs and safety concerns. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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22 pages, 2583 KiB

Open AccessArticle

Prediction of Blast-Induced Ground Vibration at a Limestone Quarry: An Artificial Intelligence Approach

by Clement Kweku Arthur, Ramesh Murlidhar Bhatawdekar, Edy Tonnizam Mohamad, Mohanad Muayad Sabri Sabri, Manish Bohra, Manoj Khandelwal and Sangki Kwon

Appl. Sci. 2022, 12(18), 9189; https://doi.org/10.3390/app12189189 - 14 Sep 2022

Cited by 6 | Viewed by 2203

Abstract

Ground vibration is one of the most unfavourable environmental effects of blasting activities, which can cause serious damage to neighboring homes and structures. As a result, effective forecasting of their severity is critical to controlling and reducing their recurrence. There are several conventional [...] Read more.

Ground vibration is one of the most unfavourable environmental effects of blasting activities, which can cause serious damage to neighboring homes and structures. As a result, effective forecasting of their severity is critical to controlling and reducing their recurrence. There are several conventional vibration predictor equations available proposed by different researchers but most of them are based on only two parameters, i.e., explosive charge used per delay and distance between blast face to the monitoring point. It is a well-known fact that blasting results are influenced by a number of blast design parameters, such as burden, spacing, powder factor, etc. but these are not being considered in any of the available conventional predictors and due to that they show a high error in predicting blast vibrations. Nowadays, artificial intelligence has been widely used in blast engineering. Thus, three artificial intelligence approaches, namely Gaussian process regression (GPR), extreme learning machine (ELM) and backpropagation neural network (BPNN) were used in this study to estimate ground vibration caused by blasting in Shree Cement Ras Limestone Mine in India. To achieve that aim, 101 blasting datasets with powder factor, average depth, distance, spacing, burden, charge weight, and stemming length as input parameters were collected from the mine site. For comparison purposes, a simple multivariate regression analysis (MVRA) model as well as, a nonparametric regression-based technique known as multivariate adaptive regression splines (MARS) was also constructed using the same datasets. This study serves as a foundational study for the comparison of GPR, BPNN, ELM, MARS and MVRA to ascertain their respective predictive performances. Eighty-one (81) datasets representing 80% of the total blasting datasets were used to construct and train the various predictive models while 20 data samples (20%) were utilized for evaluating the predictive capabilities of the developed predictive models. Using the testing datasets, major indicators of performance, namely mean squared error (MSE), variance accounted for (VAF), correlation coefficient (R) and coefficient of determination (R²) were compared as statistical evaluators of model performance. This study revealed that the GPR model exhibited superior predictive capability in comparison to the MARS, BPNN, ELM and MVRA. The GPR model showed the highest VAF, R and R² values of 99.1728%, 0.9985 and 0.9971 respectively and the lowest MSE of 0.0903. As a result, the blast engineer can employ GPR as an effective and appropriate method for forecasting blast-induced ground vibration. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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17 pages, 4895 KiB

Open AccessArticle

Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks

by Masoud Samaei, Timur Massalow, Ali Abdolhosseinzadeh, Saffet Yagiz and Mohanad Muayad Sabri Sabri

Appl. Sci. 2022, 12(18), 9187; https://doi.org/10.3390/app12189187 - 14 Sep 2022

Cited by 1 | Viewed by 1455

Abstract

Due to the different challenges in rock sampling and in measuring their thermal conductivity (TC) in the field and laboratory, the determination of the TC of rocks using non-invasive methods is in demand in engineering projects. The relationship between TC and non-destructive tests [...] Read more.

Due to the different challenges in rock sampling and in measuring their thermal conductivity (TC) in the field and laboratory, the determination of the TC of rocks using non-invasive methods is in demand in engineering projects. The relationship between TC and non-destructive tests has not been well-established. An investigation of the most important variables affecting the TC values for rocks was conducted in this study. Currently, the black-boxed models for TC prediction are being replaced with artificial intelligence-based models, with mathematical equations to fill the gap caused by the lack of a tangible model for future studies and developments. In this regard, two models were developed based on which gene expression programming (GEP) algorithms and non-linear multivariable regressions (NLMR) were utilized. When comparing the performances of the proposed models to that of other previously published models, it was revealed that the GEP and NLMR models were able to produce more accurate predictions than other models were. Moreover, the high value of R-squared (equals 0.95) for the GEP model confirmed its superiority. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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16 pages, 3555 KiB

Open AccessArticle

Inverse Modeling of Seepage Parameters Based on an Improved Gray Wolf Optimizer

by Yongkang Shu, Zhenzhong Shen, Liqun Xu, Junrong Duan, Luyi Ju and Qi Liu

Appl. Sci. 2022, 12(17), 8519; https://doi.org/10.3390/app12178519 - 25 Aug 2022

Cited by 6 | Viewed by 1194

Abstract

The seepage parameters of the dam body and dam foundation are difficult to determine accurately and quickly. Based on the inverse analysis, a Gray Wolf Optimizer (GWO) was introduced into this study to search the target hydraulic conductivity. A novel approach for initialization, [...] Read more.

The seepage parameters of the dam body and dam foundation are difficult to determine accurately and quickly. Based on the inverse analysis, a Gray Wolf Optimizer (GWO) was introduced into this study to search the target hydraulic conductivity. A novel approach for initialization, a polynomial-based nonlinear convergence factor, and weighting factors based on Euclidean norms and hierarchy were applied to improve GWO. The practicability and effectiveness of Improved Gray Wolf Optimizer (IGWO) were evaluated by numerical experiments. Taking Kakiwa dam located on the Muli River of China as a case, an inversion analysis for seepage parameters was accomplished by adopting the proposed optimization algorithm. The simulated hydraulic heads and seepage volume agree with measurements obtained from piezometers and measuring weir. The steady seepage field of the dam was analyzed. The results indicate the feasibility of IGWO in determining the seepage parameters of Kakiwa dam. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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16 pages, 2460 KiB

Open AccessArticle

Analysis of Malicious Node Identification Algorithm of Internet of Vehicles under Blockchain Technology: A Case Study of Intelligent Technology in Automotive Engineering

by Jing Chen, Tong Li and Rui Zhu

Appl. Sci. 2022, 12(16), 8362; https://doi.org/10.3390/app12168362 - 21 Aug 2022

Cited by 2 | Viewed by 1486

Abstract

False messages sent by malicious or selfish vehicle nodes will reduce the operation efficiency of the Internet of Vehicles, and can even endanger drivers in serious cases. Therefore, it is very important to detect malicious vehicle nodes in the network in a timely [...] Read more.

False messages sent by malicious or selfish vehicle nodes will reduce the operation efficiency of the Internet of Vehicles, and can even endanger drivers in serious cases. Therefore, it is very important to detect malicious vehicle nodes in the network in a timely manner. At present, the existing research on detecting malicious vehicle nodes in the Internet of Vehicles has some problems, such as difficulties with identification and a low detection efficiency. Blockchain technology cannot be tampered with or deleted and has open and transparent characteristics. Therefore, as a shared distributed ledger in decentralized networking, blockchain can promote collaboration between transactions, processing and interaction equipment, and help to establish a scalable, universal, private, secure and reliable car networking system. This paper puts forward a block-network-based malicious node detection mechanism. Using blockchain technology in a car network for malicious node identification algorithm could create a security scheme that can ensure smooth communication between network vehicles. A consensus on legal vehicle identification, message integrity verification, false message identification and malicious vehicle node identification form the four parts of the security scheme. Based on the public–private key mechanism and RSA encryption algorithm, combined with the malicious node identification algorithm in the Internet of Vehicles, the authenticity of the vehicle’s identity and message is determined to protect the vehicle’s security and privacy. First, a blockchain-based, malicious node detection architecture is constructed for the Internet of vehicles. We propose a malicious node identification algorithm based on the blockchain consensus mechanism. Combined the above detection architecture with the consensus mechanism, a comprehensive and accurate verification of vehicle identity and message authenticity is ensured, looking at the four aspects of vehicle identification, accounting node selection, verification of transmission message integrity and identification of the authenticity of transmission messages. Subsequently, the verification results will be globally broadcast in the Internet of Vehicles to suppress malicious behavior, further ensure that reliable event messages are provided for the driver, improve the VANET operation environment, and improve the operation efficiency of the Internet of Vehicles. Comparing the proposed detection mechanism using simulation software, the simulation results show that the proposed blockchain-based trust detection mechanism can effectively improve the accuracy of vehicle node authentication and identification of false messages, and improve network transmission performance in the Internet of Vehicles environment. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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23 pages, 6081 KiB

Open AccessArticle

Multifunctional Models, Including an Artificial Neural Network, to Predict the Compressive Strength of Self-Compacting Concrete

by Kawan Ghafor

Appl. Sci. 2022, 12(16), 8161; https://doi.org/10.3390/app12168161 - 15 Aug 2022

Cited by 5 | Viewed by 1470

Abstract

In this study, three different models were developed to predict the compressive strength of SCC, including the nonlinear relationship (NLR) model, multiregression model (MLR), and artificial neural network. Thus, a set of 400 data were collected and analyzed to evaluate the effect of [...] Read more.

In this study, three different models were developed to predict the compressive strength of SCC, including the nonlinear relationship (NLR) model, multiregression model (MLR), and artificial neural network. Thus, a set of 400 data were collected and analyzed to evaluate the effect of seven variables that have a direct impact on the CS, such as water to cement ratio (w/c), cement content (C, kg/m³), gravel content (G, kg/m³), sand content (S, kg/m³), fly ash content, (FA, kg/m³), superplasticizer content (SP, kg/m³), and curing time (t, days) up to 365 days. Several statistical assessment parameters, such as the coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), and scatter index (SI), were used to assess the performance of the predicted models. Depending on the statistical analysis, the median percentage of superplasticizers for the production of SCC was 1.33%. Furthermore, the percentage of fly ash inside all mixes ranged from 0 to 100%, with 1 to 365 days of curing and sand content ranging from 845 to 1066 kg/m³. The results indicated that ANN performed better than other models with the lowest SI values. Curing time has the most impact on forecasts for the CS of SCC modified with FA. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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18 pages, 3315 KiB

Open AccessArticle

Predicting Rock Brittleness Using a Robust Evolutionary Programming Paradigm and Regression-Based Feature Selection Model

by Mehdi Jamei, Ahmed Salih Mohammed, Iman Ahmadianfar, Mohanad Muayad Sabri Sabri, Masoud Karbasi and Mahdi Hasanipanah

Appl. Sci. 2022, 12(14), 7101; https://doi.org/10.3390/app12147101 - 14 Jul 2022

Cited by 10 | Viewed by 1550

Abstract

Brittleness plays an important role in assessing the stability of the surrounding rock mass in deep underground projects. To this end, the present study deals with developing a robust evolutionary programming paradigm known as linear genetic programming (LGP) for estimating the brittleness index [...] Read more.

Brittleness plays an important role in assessing the stability of the surrounding rock mass in deep underground projects. To this end, the present study deals with developing a robust evolutionary programming paradigm known as linear genetic programming (LGP) for estimating the brittleness index (BI). In addition, the bootstrap aggregate (Bagged) regression tree (BRT) and two efficient lazy machine learning approaches, namely local weighted linear regression (LWLR) and KStar approach, were examined to validate the LGP model. To the best of our knowledge, this is the first attempt to estimate the BI through the LGP model. A tunneling project in Pahang state, Malaysia, was investigated, and the requirement datasets were measured to construct the proposed models. According to the results from the testing phase, the LGP model yielded the best statistical indicators (R = 0.9529, RMSE = 0.4838, and I_A = 0.9744) for modeling BI, followed by LWLR (R = 0.9490, RMSE = 0.6607, and I_A = 0.9400), BRT (R = 0.9433, RMSE = 0.6875, and I_A = 0.9324), and KStar (R = 0.9310, RMSE = 0.7933, and I_A = 0.9095), respectively. In addition, the sensitivity analysis demonstrated that the dry density factor demonstrated the most effective prediction of BI. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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23 pages, 4615 KiB

Open AccessArticle

Adaptive Salp Swarm Algorithm for Optimization of Geotechnical Structures

by Mohammad Khajehzadeh, Amin Iraji, Ali Majdi, Suraparb Keawsawasvong and Moncef L. Nehdi

Appl. Sci. 2022, 12(13), 6749; https://doi.org/10.3390/app12136749 - 03 Jul 2022

Cited by 13 | Viewed by 1859

Abstract

Based on the salp swarm algorithm (SSA), this paper proposes an efficient metaheuristic algorithm for solving global optimization problems and optimizing two commonly encountered geotechnical engineering structures: reinforced concrete cantilever retaining walls and shallow spread foundations. Two new equations for the leader- and [...] Read more.

Based on the salp swarm algorithm (SSA), this paper proposes an efficient metaheuristic algorithm for solving global optimization problems and optimizing two commonly encountered geotechnical engineering structures: reinforced concrete cantilever retaining walls and shallow spread foundations. Two new equations for the leader- and followers-position-updating procedures were introduced in the proposed adaptive salp swarm optimization (ASSA). This change improved the algorithm’s exploration capabilities while preventing it from converging prematurely. Benchmark test functions were used to confirm the proposed algorithm’s performance, and the results were compared to the SSA and other effective optimization algorithms. A Wilcoxon’s rank sum test was performed to evaluate the pairwise statistical performances of the algorithms, and it indicated the significant superiority of the ASSA. The new algorithm can also be used to optimize low-cost retaining walls and foundations. In the analysis and design procedures, both geotechnical and structural limit states were used. Two case studies of retaining walls and spread foundations were solved using the proposed methodology. According to the simulation results, ASSA outperforms alternative models and demonstrates the ability to produce better optimal solutions. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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26 pages, 5222 KiB

Open AccessArticle

Estimation of Blast-Induced Peak Particle Velocity through the Improved Weighted Random Forest Technique

by Biao He, Sai Hin Lai, Ahmed Salih Mohammed, Mohanad Muayad Sabri Sabri and Dmitrii Vladimirovich Ulrikh

Appl. Sci. 2022, 12(10), 5019; https://doi.org/10.3390/app12105019 - 16 May 2022

Cited by 9 | Viewed by 1901

Abstract

Blasting is one of the primary aspects of the mining operations, and its environmental effects interfere with the safety of lives and property. Therefore, it is essential to accurately estimate the environmental impact of blasting, i.e., peak particle velocity (PPV). In this study, [...] Read more.

Blasting is one of the primary aspects of the mining operations, and its environmental effects interfere with the safety of lives and property. Therefore, it is essential to accurately estimate the environmental impact of blasting, i.e., peak particle velocity (PPV). In this study, a regular random forest (RF) model was developed using 102 blasting samples that were collected from an open granite mine. The model inputs included six parameters, while the output is PPV. Then, to improve the performance of the regular RF model, five techniques, i.e., refined weights based on the accuracy of decision trees and the optimization of three metaheuristic algorithms, were proposed to enhance the predictive capability of the regular RF model. The results showed that all refined weighted RF models have better performance than the regular RF model. In particular, the refined weighted RF model using the whale optimization algorithm (WOA) showed the best performance. Moreover, the sensitivity analysis results revealed that the powder factor (PF) has the most significant impact on the prediction of the PPV in this project case, which means that the magnitude of the PPV can be managed by controlling the size of the PF. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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12 pages, 3548 KiB

Open AccessArticle

Ultra-Scratch-Resistant, Hydrophobic and Transparent Organosilicon-Epoxy-Resin Coating with a Double Cross-Link Structure

by Zeyu Qiu, Haofeng Lin, Longlong Zeng, Yunfeng Liang, Chunhong Zeng and Ruijiang Hong

Appl. Sci. 2022, 12(10), 4854; https://doi.org/10.3390/app12104854 - 11 May 2022

Cited by 1 | Viewed by 2056

Abstract

In this paper, an ultra-scratch-resistant, hydrophobic and transparent coating was fabricated by the sol–gel method using (3-Glycidyloxypropyl) triethoxysilane (GPTES) and curing agents. When the silanol was condensated, the ring-opening reaction of the epoxy groups also took place, which formed a double-cross-linked network (Si–O–Si [...] Read more.

In this paper, an ultra-scratch-resistant, hydrophobic and transparent coating was fabricated by the sol–gel method using (3-Glycidyloxypropyl) triethoxysilane (GPTES) and curing agents. When the silanol was condensated, the ring-opening reaction of the epoxy groups also took place, which formed a double-cross-linked network (Si–O–Si and R₃N). This network structure restricted the molecule chains from being twisted or dislocated, resulting in a great improvement of the abrasion resistance of the coating. A pencil hardness grade up to 8H was obtained. The coating also showed excellent stability after being soaked in pH = 2 and pH = 12 solutions, seawater and acetone, respectively. In addition, a water contact angle of 121° was obtained by post-treatment with hexamethyldisilazane (HMDS). The average transmittance of the coating reached to 90% in the wavelength range of 400~800 nm, nearly identical to the glass substrate. With multiple desirable properties and a simple fabrication process, this low-cost coating shows great potential in many practical applications. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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16 pages, 5155 KiB

Open AccessArticle

Soil Classification from Piezocone Penetration Test Using Fuzzy Clustering and Neuro-Fuzzy Theory

by Joon-Shik Moon, Chan-Hong Kim and Young-Sang Kim

Appl. Sci. 2022, 12(8), 4023; https://doi.org/10.3390/app12084023 - 15 Apr 2022

Cited by 2 | Viewed by 1638

Abstract

The advantage of the piezocone penetration test is a guarantee of continuous data, which are a source of reliable interpretation of the target soil layer. Much research has been carried out for several decades, and several classification charts have been developed to classify [...] Read more.

The advantage of the piezocone penetration test is a guarantee of continuous data, which are a source of reliable interpretation of the target soil layer. Much research has been carried out for several decades, and several classification charts have been developed to classify in situ soil from the cone penetration test result. Even though most present classification charts or methods were developed on the basis of data which were compiled over many countries, they should be verified to be feasible for local country. However, unfortunately, revision of those charts is quite difficult or almost impossible even though a chart provides misclassified soil class. In this research, a new method for developing soil classification model is proposed by using soft computing theory—fuzzy C-mean clustering and neuro-fuzzy theory—as a function of 5173 piezocone penetration test (PCPT) results and soil boring logs compiled from 17 local sites around Korea. Feasibility of the proposed soil classification model was verified from the viewpoint of accuracy of the classification result by comparing the classification results not only for data which were used for developing the model but also new data, which were not included in developing the model with real boring logs, other fuzzy computing classification models, and Robertson’s charts. The biggest advantage of the proposed method is that it is easy to make the piezocone soil classification system more accurate by updating new data. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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26 pages, 9200 KiB

Open AccessArticle

A Design of CGK-Based Granular Model Using Hierarchical Structure

by Chan-Uk Yeom and Keun-Chang Kwak

Appl. Sci. 2022, 12(6), 3154; https://doi.org/10.3390/app12063154 - 19 Mar 2022

Cited by 1 | Viewed by 1804

Abstract

In this paper, we propose context-based GK clustering and design a CGK-based granular model and a hierarchical CGK-based granular model. Existing fuzzy clustering generates clusters using Euclidean distances. However, there is a problem in that performance decreases when a cluster is created from [...] Read more.

In this paper, we propose context-based GK clustering and design a CGK-based granular model and a hierarchical CGK-based granular model. Existing fuzzy clustering generates clusters using Euclidean distances. However, there is a problem in that performance decreases when a cluster is created from data with strong nonlinearity. To improve this problem, GK clustering is used. GK clustering creates clusters using Mahalanobis distance. In this paper, we propose context-based GK (CGK) clustering, which adds a method that considers the output space in the existing GK clustering, to create a cluster that considers not only the input space but also the output space. there is. Based on the proposed CGK clustering, a CGK-based granular model and a hierarchical CGK-based granular model were designed. Since the output of the CGK-based granular model is in the form of a context, it has the advantage of verbally expressing the prediction result, and the CGK-based granular model with a hierarchical structure can generate high-dimensional information granules, so meaningful information with high abstraction value granules can be created. In order to verify the validity of the method proposed in this paper, as a result of conducting an experiment using the concrete compressive strength database, it was confirmed that the proposed methods showed superior performance than the existing granular models. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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18 pages, 9714 KiB

Open AccessArticle

Development of Open-Assistant Environment for Integrated Operation of 3D Bridge Model and Engineering Document Information

by Sang I. Park, Bong-Geun Kim, Wonhui Goh and Goangseup Zi

Appl. Sci. 2022, 12(5), 2510; https://doi.org/10.3390/app12052510 - 28 Feb 2022

Viewed by 1628

Abstract

This study proposes a method for assistant environments to integrate 3D bridge model information and engineering document fragments. The engineering document content varies depending on the process. Therefore, we accept a loose coupling concept to support the independence of each information set instead [...] Read more.

This study proposes a method for assistant environments to integrate 3D bridge model information and engineering document fragments. The engineering document content varies depending on the process. Therefore, we accept a loose coupling concept to support the independence of each information set instead of using a specific data model for effective integration. The engineering document is translated into an Extensible Markup Language (XML)-based structured format based on the explicit and apparent semantic structure of the document. An extended industry foundation classes (IFC) schema is proposed to manage the bridge information model, as well as document fragments. An information document (iMapDoc) is proposed to manage interim data to connect a 3D digital model, an IFC model, and engineering document fragments. Document fragments on a specific component in the 3D bridge model are retrieved to validate the developed integrated assistant module. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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18 pages, 1552 KiB

Open AccessArticle

Slope Stability Classification under Seismic Conditions Using Several Tree-Based Intelligent Techniques

by Panagiotis G. Asteris, Fariz Iskandar Mohd Rizal, Mohammadreza Koopialipoor, Panayiotis C. Roussis, Maria Ferentinou, Danial Jahed Armaghani and Behrouz Gordan

Appl. Sci. 2022, 12(3), 1753; https://doi.org/10.3390/app12031753 - 08 Feb 2022

Cited by 46 | Viewed by 4247

Abstract

Slope stability analysis allows engineers to pinpoint risky areas, study trigger mechanisms for slope failures, and design slopes with optimal safety and reliability. Before the widespread usage of computers, slope stability analysis was conducted through semi analytical methods, or stability charts. Presently, engineers [...] Read more.

Slope stability analysis allows engineers to pinpoint risky areas, study trigger mechanisms for slope failures, and design slopes with optimal safety and reliability. Before the widespread usage of computers, slope stability analysis was conducted through semi analytical methods, or stability charts. Presently, engineers have developed many computational tools to perform slope stability analysis more efficiently. The challenge associated with furthering slope stability methods is to create a reliable design solution to perform reliable estimations involving a number of geometric and mechanical variables. The objective of this study was to investigate the application of tree-based models, including decision tree (DT), random forest (RF), and AdaBoost, in slope stability classification under seismic loading conditions. The input variables used in the modelling were slope height, slope inclination, cohesion, friction angle, and peak ground acceleration to classify safe slopes and unsafe slopes. The training data for the developed computational intelligence models resulted from a series of slope stability analyses performed using a standard geotechnical engineering software commonly used in geotechnical engineering practice. Upon construction of the tree-based models, the model assessment was performed through the use and calculation of accuracy, F1-score, recall, and precision indices. All tree-based models could efficiently classify the slope stability status, with the AdaBoost model providing the highest performance for the classification of slope stability for both model development and model assessment parts. The proposed AdaBoost model can be used as a screening tool during the stage of feasibility studies of related infrastructure projects, to classify slopes according to their expected status of stability under seismic loading conditions. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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25 pages, 11341 KiB

Open AccessArticle

Application of Soft Computing Techniques to Estimate Cutter Life Index Using Mechanical Properties of Rocks

by Timur Massalov, Saffet Yagiz and Amoussou Coffi Adoko

Appl. Sci. 2022, 12(3), 1446; https://doi.org/10.3390/app12031446 - 28 Jan 2022

Cited by 6 | Viewed by 2837

Abstract

The wear of cutting tools is critical for any engineering applications dealing with mechanical rock excavations, as it directly affects the cost and time of project completion as well as the utilization rate of excavators in various rock masses. The cutting tool wear [...] Read more.

The wear of cutting tools is critical for any engineering applications dealing with mechanical rock excavations, as it directly affects the cost and time of project completion as well as the utilization rate of excavators in various rock masses. The cutting tool wear could be expressed in terms of the life of the tool used to excavate rocks in hours or cutter per unit volume of excavated materials. The aim of this study is to estimate disc cutter wear as a function of common mechanical rock properties including uniaxial compressive strength, Brazilian tensile strength, brittleness, and density. To achieve this goal, a database of cutter life was established by analyzing data from 80 tunneling projects. The data were then utilized for evaluating the relationship between rock properties and cutter consumption by means of cutter life index. The analysis was based on artificial intelligence techniques, namely artificial neural networks (ANN) and fuzzy logic (FL). Furthermore, linear and non-linear regression methods were also used to investigate the relationship between these parameters using a statistical software package. Several alternative models are introduced with different input variables for each model, to identify the best model with the highest accuracy. To develop these models, 70% of the dataset was used for training and the rest, for testing. The estimated cutter life by various models was compared with each other to identify the most reliable model. It appears that the ANN and FL techniques are superior to standard linear and non-linear multiple regression analysis, based on the higher correlation coefficient (R²) and lower Mean square error (MSE). Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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14 pages, 1578 KiB

Open AccessArticle

Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model

by Yufeng Qian, Mahdi Aghaabbasi, Mujahid Ali, Muwaffaq Alqurashi, Bashir Salah, Rosilawati Zainol, Mehdi Moeinaddini and Enas E. Hussein

Appl. Sci. 2021, 11(24), 11916; https://doi.org/10.3390/app112411916 - 15 Dec 2021

Cited by 18 | Viewed by 2516

Abstract

The investigation of travel mode choice is an essential task in transport planning and policymaking for predicting travel demands. Typically, mode choice datasets are imbalanced and learning from such datasets is challenging. This study deals with imbalanced mode choice data by developing an [...] Read more.

The investigation of travel mode choice is an essential task in transport planning and policymaking for predicting travel demands. Typically, mode choice datasets are imbalanced and learning from such datasets is challenging. This study deals with imbalanced mode choice data by developing an algorithm (SVM_AK) based on a support vector machine model and the theory of adjusting kernel scaling. The kernel function’s choice was evaluated by applying the likelihood-ratio chi-square and weighting measures. The empirical assessment was performed on the 2017 National Household Travel Survey–California dataset. The performance of the SVM_AK model was compared with several other models, including neural networks, XGBoost, Bayesian Network, standard support vector machine model, and some SVM-based models that were previously developed to handle the imbalanced datasets. The SVM_AK model outperformed these models, and in some cases improved the accuracy of the minority class classification. For the majority class, the accuracy improvement was substantial. This algorithm can be applied to other tasks in the transport planning domain that deal with uneven data distribution. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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15 pages, 2274 KiB

Open AccessArticle

Use of the Gene-Expression Programming Equation and FEM for the High-Strength CFST Columns

by Huanjun Jiang, Ahmed Salih Mohammed, Reza Andasht Kazeroon and Payam Sarir

Appl. Sci. 2021, 11(21), 10468; https://doi.org/10.3390/app112110468 - 08 Nov 2021

Cited by 20 | Viewed by 1772

Abstract

The ultimate strength of composite columns is a significant factor for engineers and, therefore, finding a trustworthy and quick method to predict it with a good accuracy is very important. In the previous studies, the gene expression programming (GEP), as a new methodology, [...] Read more.

The ultimate strength of composite columns is a significant factor for engineers and, therefore, finding a trustworthy and quick method to predict it with a good accuracy is very important. In the previous studies, the gene expression programming (GEP), as a new methodology, was trained and tested for a number of concrete-filled steel tube (CFST) samples and a GEP-based equation was proposed to estimate the ultimate bearing capacity of the CFST columns. In this study, however, the equation is considered to be validated for its results, and to ensure it is clearly capable of predicting the ultimate bearing capacity of the columns with high-strength concrete. Therefore, 32 samples with high-strength concrete were considered and they were modelled using the finite element method (FEM). The ultimate bearing capacity was obtained by FEM, and was compared with the results achieved from the GEP equation, and both were compared to the respective experimental results. It was evident from the results that the majority of values obtained from GEP were closer to the real experimental data than those obtained from FEM. This demonstrates the accuracy of the predictive equation obtained from GEP for these types of CFST column. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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Review

Jump to: Research

25 pages, 2051 KiB

Open AccessReview

Designing for Hybrid Intelligence: A Taxonomy and Survey of Crowd-Machine Interaction

by António Correia, Andrea Grover, Daniel Schneider, Ana Paula Pimentel, Ramon Chaves, Marcos Antonio de Almeida and Benjamim Fonseca

Appl. Sci. 2023, 13(4), 2198; https://doi.org/10.3390/app13042198 - 08 Feb 2023

Cited by 2 | Viewed by 2749

Abstract

With the widespread availability and pervasiveness of artificial intelligence (AI) in many application areas across the globe, the role of crowdsourcing has seen an upsurge in terms of importance for scaling up data-driven algorithms in rapid cycles through a relatively low-cost distributed workforce [...] Read more.

With the widespread availability and pervasiveness of artificial intelligence (AI) in many application areas across the globe, the role of crowdsourcing has seen an upsurge in terms of importance for scaling up data-driven algorithms in rapid cycles through a relatively low-cost distributed workforce or even on a volunteer basis. However, there is a lack of systematic and empirical examination of the interplay among the processes and activities combining crowd-machine hybrid interaction. To uncover the enduring aspects characterizing the human-centered AI design space when involving ensembles of crowds and algorithms and their symbiotic relations and requirements, a Computer-Supported Cooperative Work (CSCW) lens strongly rooted in the taxonomic tradition of conceptual scheme development is taken with the aim of aggregating and characterizing some of the main component entities in the burgeoning domain of hybrid crowd-AI centered systems. The goal of this article is thus to propose a theoretically grounded and empirically validated analytical framework for the study of crowd-machine interaction and its environment. Based on a scoping review and several cross-sectional analyses of research studies comprising hybrid forms of human interaction with AI systems and applications at a crowd scale, the available literature was distilled and incorporated into a unifying framework comprised of taxonomic units distributed across integration dimensions that range from the original time and space axes in which every collaborative activity take place to the main attributes that constitute a hybrid intelligence architecture. The upshot is that when turning to the challenges that are inherent in tasks requiring massive participation, novel properties can be obtained for a set of potential scenarios that go beyond the single experience of a human interacting with the technology to comprise a vast set of massive machine-crowd interactions. Full article

(This article belongs to the Special Issue Novel Hybrid Intelligence Techniques in Engineering)

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