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The novel coronavirus (SARS-CoV-2) has spread at an unprecedented rate, resulting in a global pandemic (COVID-19) that has strained healthcare systems and claimed many lives. Front-line healthcare workers are among the most at risk of contracting and spreading the virus due to close contact with infected patients and settings of high viral loads. To provide these workers with an extra layer of protection, the authors propose a low-cost, prefabricated, and portable sanitising chamber that sprays individuals with sanitising fluid to disinfect clothing and external surfaces on their person. The study discusses computer-aided design of the chamber to improve uniformity of sanitiser deposition and reduce discomfort due to excessive moisture. Advanced computational fluid dynamics is used to simulate the dispersion and deposition of spray particle, and the resulting wetting pattern on the treated person is used to optimise the chamber design.
Yousef Abu-Zidan; Kate Nguyen; Priyan Mendis; Sujeeva Setunge; Hojjat Adeli. DESIGN OF A SMART PREFABRICATED SANITISING CHAMBER FOR COVID-19 USING COMPUTATIONAL FLUID DYNAMICS. JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 2021, 27, 139 -148.
AMA StyleYousef Abu-Zidan, Kate Nguyen, Priyan Mendis, Sujeeva Setunge, Hojjat Adeli. DESIGN OF A SMART PREFABRICATED SANITISING CHAMBER FOR COVID-19 USING COMPUTATIONAL FLUID DYNAMICS. JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT. 2021; 27 (2):139-148.
Chicago/Turabian StyleYousef Abu-Zidan; Kate Nguyen; Priyan Mendis; Sujeeva Setunge; Hojjat Adeli. 2021. "DESIGN OF A SMART PREFABRICATED SANITISING CHAMBER FOR COVID-19 USING COMPUTATIONAL FLUID DYNAMICS." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 27, no. 2: 139-148.
A new EEG-based methodology is presented for differential diagnosis of the Alzheimer’s disease (AD), Mild Cognitive Impairment (MCI), and healthy subjects employing the discrete wavelet transform (DWT), dispersion entropy index (DEI), a recently-proposed nonlinear measurement, and a fuzzy logic-based classification algorithm. The effectiveness and usefulness of the proposed methodology are evaluated by employing a database of measured EEG data acquired from 135 subjects, 45 MCI, 45 AD and 45 healthy subjects. The proposed methodology differentiates MCI and AD patients from HC subjects with an accuracy of 82.6−86.9%, sensitivity of 91 %, and specificity of 87 %.
Juan P. Amezquita-Sanchez; Nadia Mammone; Francesco C. Morabito; Hojjat Adeli. A New dispersion entropy and fuzzy logic system methodology for automated classification of dementia stages using electroencephalograms. Clinical Neurology and Neurosurgery 2020, 201, 106446 .
AMA StyleJuan P. Amezquita-Sanchez, Nadia Mammone, Francesco C. Morabito, Hojjat Adeli. A New dispersion entropy and fuzzy logic system methodology for automated classification of dementia stages using electroencephalograms. Clinical Neurology and Neurosurgery. 2020; 201 ():106446.
Chicago/Turabian StyleJuan P. Amezquita-Sanchez; Nadia Mammone; Francesco C. Morabito; Hojjat Adeli. 2020. "A New dispersion entropy and fuzzy logic system methodology for automated classification of dementia stages using electroencephalograms." Clinical Neurology and Neurosurgery 201, no. : 106446.
In the Big Data Era, recommender systems perform a fundamental role in data management and information filtering. In this context, Collaborative Filtering (CF) persists as one of the most prominent strategies to effectively deal with large datasets and is capable of offering users interesting content in a recommendation fashion. Nevertheless, it is well‐known CF recommenders suffer from data sparsity, mainly in cold‐start scenarios, substantially reducing the quality of recommendations. In the vast literature about the aforementioned topic, there are numerous solutions, in which the state‐of‐the‐art contributions are, in some sense, conditioned or associated with traditional CF methods such as Matrix Factorization (MF), that is, they rely on linear optimization procedures to model users and items into low‐dimensional embeddings. To overcome the aforementioned challenges, there has been an increasing number of studies exploring deep learning techniques in the CF context for latent factor modelling. In this research, authors conduct a systematic review focusing on state‐of‐the‐art literature on deep learning techniques applied in collaborative filtering recommendation, and also featuring primary studies related to mitigating the cold start problem. Additionally, authors considered the diverse non‐linear modelling strategies to deal with rating data and side information, the combination of deep learning techniques with traditional CF‐based linear methods, and an overview of the most used public datasets and evaluation metrics concerning CF scenarios.
Guilherme Brandão Martins; João Paulo Papa; Hojjat Adeli. Deep learning techniques for recommender systems based on collaborative filtering. Expert Systems 2020, 37, 1 .
AMA StyleGuilherme Brandão Martins, João Paulo Papa, Hojjat Adeli. Deep learning techniques for recommender systems based on collaborative filtering. Expert Systems. 2020; 37 (6):1.
Chicago/Turabian StyleGuilherme Brandão Martins; João Paulo Papa; Hojjat Adeli. 2020. "Deep learning techniques for recommender systems based on collaborative filtering." Expert Systems 37, no. 6: 1.
Few studies in the literature have researched the use of surface electromyography (sEMG) for motor assessment post-stroke due to the complexity of this type of signal. However, recent advances in signal processing and machine learning have provided fresh opportunities for analyzing complex, non-linear, non-stationary signals, such as sEMG. This paper presents a method for identification of the upper limb movements from sEMG signals using a combination of digital signal processing, that is discrete wavelet transform, and the enhanced probabilistic neural network (EPNN). To explore the potential of sEMG signals for monitoring motor rehabilitation progress, this study used sEMG signals from a subset of movements of the Arm Motor Ability Test (AMAT) as inputs into a movement classification algorithm. The importance of a particular frequency domain feature, that is the ratio of the mean absolute values between sub-bands, was discovered in this work. An average classification accuracy of 75.5% was achieved using the proposed approach with a maximum accuracy of 100%. The performance of the proposed method was compared with results obtained using three other classification algorithms: support vector machine (SVM), k-Nearest Neighbors (k-NN), and probabilistic neural network (PNN) in terms of sEMG movement classification. The study demonstrated the capability of using upper limb sEMG signals to identify and distinguish between functional movements used in standard upper limb motor assessments for stroke patients. The classification algorithm used in the proposed method, EPNN, outperformed SVM, k-NN, and PNN.
Alexis Burns; Hojjat Adeli; John A. Buford. Upper Limb Movement Classification Via Electromyographic Signals and an Enhanced Probabilistic Network. Journal of Medical Systems 2020, 44, 1 -12.
AMA StyleAlexis Burns, Hojjat Adeli, John A. Buford. Upper Limb Movement Classification Via Electromyographic Signals and an Enhanced Probabilistic Network. Journal of Medical Systems. 2020; 44 (10):1-12.
Chicago/Turabian StyleAlexis Burns; Hojjat Adeli; John A. Buford. 2020. "Upper Limb Movement Classification Via Electromyographic Signals and an Enhanced Probabilistic Network." Journal of Medical Systems 44, no. 10: 1-12.
This study aims to analyze the sensitivity and robustness of two Artificial Intelligence (AI) techniques, namely Gaussian Process Regression (GPR) with five different kernels (Matern32, Matern52, Exponential, Squared Exponential, and Rational Quadratic) and an Artificial Neural Network (ANN) using a Monte Carlo simulation for prediction of High-Performance Concrete (HPC) compressive strength. To this purpose, 1030 samples were collected, including eight input parameters (contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregates, fine aggregates, and concrete age) and an output parameter (the compressive strength) to generate the training and testing datasets. The proposed AI models were validated using several standard criteria, namely coefficient of determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). To analyze the sensitivity and robustness of the models, Monte Carlo simulations were performed with 500 runs. The results showed that the GPR using the Matern32 kernel function outperforms others. In addition, the sensitivity analysis showed that the content of cement and the testing age of the HPC were the most sensitive and important factors for the prediction of HPC compressive strength. In short, this study might help in selecting suitable AI models and appropriate input parameters for accurate and quick estimation of the HPC compressive strength.
Dong Van Dao; Hojjat Adeli; Hai-Bang Ly; Lu Minh Le; Vuong Minh Le; Tien-Thinh Le; Binh Thai Pham. A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation. Sustainability 2020, 12, 830 .
AMA StyleDong Van Dao, Hojjat Adeli, Hai-Bang Ly, Lu Minh Le, Vuong Minh Le, Tien-Thinh Le, Binh Thai Pham. A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation. Sustainability. 2020; 12 (3):830.
Chicago/Turabian StyleDong Van Dao; Hojjat Adeli; Hai-Bang Ly; Lu Minh Le; Vuong Minh Le; Tien-Thinh Le; Binh Thai Pham. 2020. "A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation." Sustainability 12, no. 3: 830.
Introduction: The diagnosis of epilepsy takes a certain process, depending entirely on the attending physician. However, the human factor may cause erroneous diagnosis in the analysis of the EEG signal. In the past 2 decades, many advanced signal processing and machine learning methods have been developed for the detection of epileptic seizures. However, many of these methods require large data sets and complex operations. Methods: In this study, an end-to-end machine learning model is presented for detection of epileptic seizure using the pretrained deep two-dimensional convolutional neural network (CNN) and the concept of transfer learning. The EEG signal is converted directly into visual data with a spectrogram and used directly as input data. Results: The authors analyzed the results of the training of the proposed pretrained AlexNet CNN model. Both binary and ternary classifications were performed without any extra procedure such as feature extraction. By performing data set creation from short-term spectrogram graphic images, the authors were able to achieve 100% accuracy for binary classification for epileptic seizure detection and 100% for ternary classification. Discussion/Conclusion: The proposed automatic identification and classification model can help in the early diagnosis of epilepsy, thus providing the opportunity for effective early treatment.
Hidir Selcuk Nogay; Hojjat Adeli. Detection of Epileptic Seizure Using Pretrained Deep Convolutional Neural Network and Transfer Learning. European Neurology 2020, 83, 602 -614.
AMA StyleHidir Selcuk Nogay, Hojjat Adeli. Detection of Epileptic Seizure Using Pretrained Deep Convolutional Neural Network and Transfer Learning. European Neurology. 2020; 83 (6):602-614.
Chicago/Turabian StyleHidir Selcuk Nogay; Hojjat Adeli. 2020. "Detection of Epileptic Seizure Using Pretrained Deep Convolutional Neural Network and Transfer Learning." European Neurology 83, no. 6: 602-614.
Background: Authors have been advocating the research ideology that a computer-aided diagnosis (CAD) system trained using lots of patient data and physiological signals and images based on adroit integration of advanced signal processing and artificial intelligence (AI)/machine learning techniques in an automated fashion can assist neurologists, neurosurgeons, radiologists, and other medical providers to make better clinical decisions. Summary: This paper presents a state-of-the-art review of research on automated diagnosis of 5 neurological disorders in the past 2 decades using AI techniques: epilepsy, Parkinson’s disease, Alzheimer’s disease, multiple sclerosis, and ischemic brain stroke using physiological signals and images. Recent research articles on different feature extraction methods, dimensionality reduction techniques, feature selection, and classification techniques are reviewed. Key Message: CAD systems using AI and advanced signal processing techniques can assist clinicians in analyzing and interpreting physiological signals and images more effectively.
U. Raghavendra; U. Rajendra Acharya; Hojjat Adeli. Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders. European Neurology 2019, 82, 41 -64.
AMA StyleU. Raghavendra, U. Rajendra Acharya, Hojjat Adeli. Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders. European Neurology. 2019; 82 (1-3):41-64.
Chicago/Turabian StyleU. Raghavendra; U. Rajendra Acharya; Hojjat Adeli. 2019. "Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders." European Neurology 82, no. 1-3: 41-64.
This paper presents the author’s perspective on four decades of computing in civil engineering. Examples of research by the author and his associates published during the past four decades are briefly described. They include artificial intelligence and expert system technology, computer-aided design and engineering (CAD/CAE), computer animation, object-oriented technology, database management, solid modelling, parallel processing and supercomputing, distributed computing on a cluster of workstations, neural networks, evolutionary computing and genetic algorithms, case-based reasoning, machine learning, fractality and chaos theory, wavelet transform, and web-based computing. It is argued that the introduction of novel computing ideas into the oldest engineering field has made the field more exciting. It has helped create new technologies such as semi-active vibration control and health monitoring of large structures and intelligent freeways, and automate processes that were unthinkable otherwise.
Hojjat Adeli. Four Decades of Computing in Civil Engineering. Lecture Notes in Civil Engineering 2019, 3 -11.
AMA StyleHojjat Adeli. Four Decades of Computing in Civil Engineering. Lecture Notes in Civil Engineering. 2019; ():3-11.
Chicago/Turabian StyleHojjat Adeli. 2019. "Four Decades of Computing in Civil Engineering." Lecture Notes in Civil Engineering , no. : 3-11.
This paper presents a novel dynamic ensemble learning (DEL) algorithm for designing ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the number of individual NNs employing a constructive strategy, the number of hidden nodes of individual NNs employing a constructive–pruning strategy, and different training samples for individual NN’s learning. For diversity, negative correlation learning has been introduced and also variation of training samples has been made for individual NNs that provide better learning from the whole training samples. The major benefits of the proposed DEL compared to existing ensemble algorithms are (1) automatic design of ensemble; (2) maintaining accuracy and diversity of NNs at the same time; and (3) minimum number of parameters to be defined by user. DEL algorithm is applied to a set of real-world classification problems such as the cancer, diabetes, heart disease, thyroid, credit card, glass, gene, horse, letter recognition, mushroom, and soybean datasets. It has been confirmed by experimental results that DEL produces dynamic NN ensembles of appropriate architecture and diversity that demonstrate good generalization ability.
Kazi Md. Rokibul Alam; Nazmul Siddique; Hojjat Adeli. A dynamic ensemble learning algorithm for neural networks. Neural Computing and Applications 2019, 32, 8675 -8690.
AMA StyleKazi Md. Rokibul Alam, Nazmul Siddique, Hojjat Adeli. A dynamic ensemble learning algorithm for neural networks. Neural Computing and Applications. 2019; 32 (12):8675-8690.
Chicago/Turabian StyleKazi Md. Rokibul Alam; Nazmul Siddique; Hojjat Adeli. 2019. "A dynamic ensemble learning algorithm for neural networks." Neural Computing and Applications 32, no. 12: 8675-8690.
An important subfield of brain–computer interface is the classification of motor imagery (MI) signals where a presumed action, for example, imagining the hands' motions, is mentally simulated. The brain dynamics of MI is usually measured by electroencephalography (EEG) due to its noninvasiveness. The next generation of brain–computer interface systems can benefit from the generative deep learning (GDL) models by providing end‐to‐end (e2e) machine learning and increasing their accuracy. In this study, to exploit the e2e‐property of deep learning models, a novel GDL methodology is proposed where only minimal objective‐free preprocessing steps are needed. Furthermore, to deal with the complicated multi‐class MI–EEG signals, an innovative multilevel GDL‐based classifying scheme is proposed. The effectiveness of the proposed model and its robustness against noisy MI–EEG signals is evaluated using two different GDL models, that is, deep belief network and stacked sparse autoencoder in e2e manner. Experimental results demonstrate the effectiveness of the proposed methodology with improved accuracy compared with the widely used filter bank common spatial patterns algorithm.
Ahmad Hassanpour; Majid Moradikia; Hojjat Adeli; Seyed Raouf Khayami; Pirooz Shamsinejadbabaki. A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals. Expert Systems 2019, 36, 1 .
AMA StyleAhmad Hassanpour, Majid Moradikia, Hojjat Adeli, Seyed Raouf Khayami, Pirooz Shamsinejadbabaki. A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals. Expert Systems. 2019; 36 (6):1.
Chicago/Turabian StyleAhmad Hassanpour; Majid Moradikia; Hojjat Adeli; Seyed Raouf Khayami; Pirooz Shamsinejadbabaki. 2019. "A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals." Expert Systems 36, no. 6: 1.
This study presents a method for form-finding and analysis of hyperelastic tensegrity structures based on a special strut finite element and unconstrained nonlinear programming. The strut element can function as a hyperelastic truss element with an initial cut in its undeformed length or as a strut element that shows constant force irrespectively of its nodal displacements. For the hyperelastic strut element, the invariants of the Right Cauchy-Green deformation tensor are written in terms of the element’s nodal displacements and the cut in the element’s undeformed length. The structure’s total potential energy is expressed as function of its nodal displacements and the cuts in the elements’ undeformed lengths. The minimization of this function is a nonlinear programming problem where the displacements are the unknowns. The form-finding procedure is performed by a static analysis where the stiffness matrix maybe singular along the path to equilibrium without causing convergence problems. The mathematical model includes the element’s cross-sectional deformation while the element moves in space, fully modelling its three-dimensional character. The constraint for incompressibility is satisfied exactly, eliminating the need for a penalty or augmented Lagrangian method.
Vinicius Arcaro; Hojjat Adeli. Form-finding and analysis of hyperelastic tensegrity structures using unconstrained nonlinear programming. Engineering Structures 2019, 191, 439 -446.
AMA StyleVinicius Arcaro, Hojjat Adeli. Form-finding and analysis of hyperelastic tensegrity structures using unconstrained nonlinear programming. Engineering Structures. 2019; 191 ():439-446.
Chicago/Turabian StyleVinicius Arcaro; Hojjat Adeli. 2019. "Form-finding and analysis of hyperelastic tensegrity structures using unconstrained nonlinear programming." Engineering Structures 191, no. : 439-446.
EEG signals obtained from Mild Cognitive Impairment (MCI) and the Alzheimer’s disease (AD) patients are visually indistinguishable. A new methodology is presented for differential diagnosis of MCI and the AD through adroit integration of a new signal processing technique, the integrated multiple signal classification and empirical wavelet transform (MUSIC-EWT), different nonlinear features such as fractality dimension (FD) from the chaos theory, and a classification algorithm, the enhanced probabilistic neural network model of Ahmadlou and Adeli using the EEG signals. Three different FD measures are investigated: Box dimension (BD), Higuchi’s FD (HFD), and Katz’s FD (KFD) along with another measure of the self-similarities of the signals known as the Hurst exponent (HE). The accuracy of the proposed method was verified using the monitored EEG signals from 37 MCI and 37 AD patients. The proposed method is compared with other methodologies presented in the literature recently. It was demonstrated that the proposed method, MUSIC-EWT algorithm combined with nonlinear features BD and HE, and the EPNN classifier can be employed for differential diagnosis of MCI and AD patients with an accuracy of 90.3%.
Juan P. Amezquita-Sanchez; Nadia Mammone; Francesco Carlo Morabito; Silvia Marino; Hojjat Adeli. A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer’s disease using EEG signals. Journal of Neuroscience Methods 2019, 322, 88 -95.
AMA StyleJuan P. Amezquita-Sanchez, Nadia Mammone, Francesco Carlo Morabito, Silvia Marino, Hojjat Adeli. A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer’s disease using EEG signals. Journal of Neuroscience Methods. 2019; 322 ():88-95.
Chicago/Turabian StyleJuan P. Amezquita-Sanchez; Nadia Mammone; Francesco Carlo Morabito; Silvia Marino; Hojjat Adeli. 2019. "A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer’s disease using EEG signals." Journal of Neuroscience Methods 322, no. : 88-95.
In the past two decades, passive control strategies using isolation and fluid dampers have been employed for the seismic protection of bridge highway structures. The isolation vibration control, however, lacks the adaptability to react in real-time for changes during unpredictable earthquake loadings. This research advances the idea of combining the conventional passive control (base isolation) with a semi-active or active control system to create the next generation of smart bridge structures. A novel control algorithm based on the evoluationary game theory concept of replicator dynamics is investigated for vibration reduction of highway bridge structures equipped with both a passive isolation system and semi-active control devices subjected to earthquake loadings. The proposed methodology is evaluated by application to a benchmark example based on Interstate 5 overcrossing California State Route 91 (abbreviated as 91/5) bridge in southern California subjected to near-field historical earthquake excitations. Substantial reduction in mid-span displacement is achieved compared with the conventional base-isolated bridge.
Mariantonieta Gutierrez Soto; Hojjat Adeli. Semi-active vibration control of smart isolated highway bridge structures using replicator dynamics. Engineering Structures 2019, 186, 536 -552.
AMA StyleMariantonieta Gutierrez Soto, Hojjat Adeli. Semi-active vibration control of smart isolated highway bridge structures using replicator dynamics. Engineering Structures. 2019; 186 ():536-552.
Chicago/Turabian StyleMariantonieta Gutierrez Soto; Hojjat Adeli. 2019. "Semi-active vibration control of smart isolated highway bridge structures using replicator dynamics." Engineering Structures 186, no. : 536-552.
In this research, the concept of fractality based on nonlinear science and chaos theory is explored to study and evaluate the complexity of speech-evoked auditory brainstem response (s-ABR) time series in order to capture its intrinsic multiscale dynamics. The visibility graph of the s-ABR series is proposed as a quantitative method to differentiate subjects with persistent developmental stuttering (PDS) from the normal group. Differential complexities between normal and PDS subjects is quantified using Graph index complexity (GIC). The model is applied to 14 individuals with PDS and 15 normal subjects. The results reveal the promising ability of GIC for assessment of abnormal activation of brainstem level in PDS group. It is observed that all s-ABR series have visibility graphs with a power-law topology and fractality in the s-ABR series is dictated by a mechanism associated with long-term memory of the auditory system dynamics at the brainstem level.
Marjan Mozaffarilegha; Hojjat Adeli. Visibility graph analysis of speech evoked auditory brainstem response in persistent developmental stuttering. Neuroscience Letters 2018, 696, 28 -32.
AMA StyleMarjan Mozaffarilegha, Hojjat Adeli. Visibility graph analysis of speech evoked auditory brainstem response in persistent developmental stuttering. Neuroscience Letters. 2018; 696 ():28-32.
Chicago/Turabian StyleMarjan Mozaffarilegha; Hojjat Adeli. 2018. "Visibility graph analysis of speech evoked auditory brainstem response in persistent developmental stuttering." Neuroscience Letters 696, no. : 28-32.
In addition to materials, labor, equipment, and method, construction cost depends on many other factors such as the project locality, type, construction duration, scheduling, and the extent of use of recycled materials. Further, the fluctuation of economic variables and indexes (EV&Is), such as liquidity, wholesale price index, and building services index, causes variation in costs. These changes may increase or reduce the construction cost, are hard to predict, and are normally ignored in the traditional cost estimation computation. This paper presents an innovative construction cost estimation model using advanced machine-learning concepts and taking into account the EV&Is. A data structure is proposed that incorporates a set of physical and financial (P&F) variables of the real estate units as well as a set of EV&Is variables affecting the construction costs. The model includes an unsupervised deep Boltzmann machine (DBM) learning approach along with a softmax layer (DBM-SoftMax), and a three-layer back-propagation neural network (BPNN) or another regression model, support vector machine (SVM). The role of DBM-SoftMax is to extract relevant features from the input data. The role of the BPNN or SVM is to turn the trained unsupervised DBM into a supervised regression network. This combination improves the effectiveness and accuracy of both conventional BPNN and SVM. A sensitivity analysis was performed within the algorithm in order to achieve the best results taking into account the impact of the EV&I factors in different times (time lags). The model was verified using the construction cost data for 372 low- and midrise buildings in the range of three to nine stories. Cost estimation errors of the proposed model were much less than those of both the BPNN-only and SVM-only models, thus demonstrating the effectiveness of the strategies employed in this research and the superiority of the proposed model.
Mohammad Hossein Rafiei; Hojjat Adeli. Novel Machine-Learning Model for Estimating Construction Costs Considering Economic Variables and Indexes. Journal of Construction Engineering and Management 2018, 144, 04018106 .
AMA StyleMohammad Hossein Rafiei, Hojjat Adeli. Novel Machine-Learning Model for Estimating Construction Costs Considering Economic Variables and Indexes. Journal of Construction Engineering and Management. 2018; 144 (12):04018106.
Chicago/Turabian StyleMohammad Hossein Rafiei; Hojjat Adeli. 2018. "Novel Machine-Learning Model for Estimating Construction Costs Considering Economic Variables and Indexes." Journal of Construction Engineering and Management 144, no. 12: 04018106.
Compaction quality assessment and control for an earth‐rock dam is the key measure to ensure dam safety. However, to date, the compaction quality assessment model has not been accurate enough, and no effective feedback control measures have been developed. Hybrid data mining algorithms have great potential for solving this problem. In this study, smart bacteria‐foraging algorithm‐based customized kernel support vector regression (SBFA‐CKSVR) is proposed for compaction quality assessment, whereas an enhanced probabilistic neural network (EPNN) is adopted for compaction quality control. SBFA integrates a bacteria‐foraging algorithm, chaos mapping, and adaptive and quantum computing to solve the high‐dimensional complex problem effectively. CKSVR is proposed to approximate a function in quadratic continuous integral space L2(R) where its hyperparameters are optimized by SBFA. Finally, SBFA‐CKSVR is used to establish a high‐precision compaction quality assessment model whereas the EPNN is adopted to realize the compaction quality feedback control. A three‐dimensional real‐time monitoring system for the earth‐rock dam is also developed based on SBFA‐CKSVR and EPNN. A large‐scale hydraulic engineering application proves the effectiveness and superiority of this research compared with the previous work.
Jiajun Wang; Denghua Zhong; Hojjat Adeli; Dong Wang; Minghui Liu. Smart bacteria-foraging algorithm-based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth-rock dam. Expert Systems 2018, 35, e12357 .
AMA StyleJiajun Wang, Denghua Zhong, Hojjat Adeli, Dong Wang, Minghui Liu. Smart bacteria-foraging algorithm-based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth-rock dam. Expert Systems. 2018; 35 (6):e12357.
Chicago/Turabian StyleJiajun Wang; Denghua Zhong; Hojjat Adeli; Dong Wang; Minghui Liu. 2018. "Smart bacteria-foraging algorithm-based customized kernel support vector regression and enhanced probabilistic neural network for compaction quality assessment and control of earth-rock dam." Expert Systems 35, no. 6: e12357.
The traditional methods for inspecting large concrete structures such as dams and cooling towers require erecting large amounts of scaffolding to access the surface of the concrete structure in order to sound the concrete with an impact device or hammer to expose the damaged or defective areas. Another method for accessing the surface of a large concrete structure is to employ climbing inspections which poses a considerable safety risk. These traditional methods are used to determine defect or damage within a few inches of the surface. In addition to the logistic difficulty of these methods a hammer can cause damage if care is not taken. Further, it can cover only a small area. Infrared Thermography (IRT), also referred to as thermal imaging, utilizes the infrared spectrum to show differences in heat dissipating from a structure using a thermal imaging camera. This paper presents a review of the IRT research for detecting defects in concrete structures. Health monitoring and damage detection of large structures such as bridges and high-rise buildings has been a very active area of research in recent years. The two main approaches explored by researchers are vibration-based health monitoring and camera-based vision technology. IRT remains to be another promising technology for economical health monitoring of structures.
Gene F. Sirca Jr.; Hojjat Adeli. INFRARED THERMOGRAPHY FOR DETECTING DEFECTS IN CONCRETE STRUCTURES. JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 2018, 24, 508 -515.
AMA StyleGene F. Sirca Jr., Hojjat Adeli. INFRARED THERMOGRAPHY FOR DETECTING DEFECTS IN CONCRETE STRUCTURES. JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT. 2018; 24 (7):508-515.
Chicago/Turabian StyleGene F. Sirca Jr.; Hojjat Adeli. 2018. "INFRARED THERMOGRAPHY FOR DETECTING DEFECTS IN CONCRETE STRUCTURES." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 24, no. 7: 508-515.
A unifying approach is presented for the nonlinear static analysis of cable structures and for the form-finding of tensegrity structures. The novelty lies in the possibility of static analyses of structures where the stiffness matrix is singular throughout the path to equilibrium. The unification of static analyzes and form-finding procedures allows the understanding and treatment of tensegrity and cable structures as a single type of structure. A total potential energy function is derived in terms of nodal displacements which are the unknowns of a nonlinear programming problem. The proposed approach uses a Quasi-Newton method overcoming a limitation of the Newton Raphson Method employed by widel-used commercial Finite Element software packages. Example analyses are presented and compared with experimental results reported in the literature to demonstrate the feasibility of the proposed approach which is particularly useful for under-constrained structures that contain pre-tensioned elements.
Nathan James Branam; Vinicius Arcaro; Hojjat Adeli. A unified approach for analysis of cable and tensegrity structures using memoryless quasi-newton minimization of total strain energy. Engineering Structures 2018, 179, 332 -340.
AMA StyleNathan James Branam, Vinicius Arcaro, Hojjat Adeli. A unified approach for analysis of cable and tensegrity structures using memoryless quasi-newton minimization of total strain energy. Engineering Structures. 2018; 179 ():332-340.
Chicago/Turabian StyleNathan James Branam; Vinicius Arcaro; Hojjat Adeli. 2018. "A unified approach for analysis of cable and tensegrity structures using memoryless quasi-newton minimization of total strain energy." Engineering Structures 179, no. : 332-340.
Virtual rehabilitation yields outcomes that are at least as good as traditional care for improving upper limb function and the capacity to carry out activities of daily living. Due to the advent of low-cost gaming systems and patient preference for game-based therapies, video game technology will likely be increasingly utilized in physical therapy practice in the coming years. Gaming systems that incorporate low-cost motion capture technology often generate large datasets of therapeutic movements performed over the course of rehabilitation. An infrastructure has yet to be established, however, to enable efficient processing of large quantities of movement data that are collected outside of a controlled laboratory setting. In this paper, a methodology is presented for extracting and evaluating therapeutic movements from game-based rehabilitation that occurs in uncontrolled and unmonitored settings. By overcoming these challenges, meaningful kinematic analysis of rehabilitation trajectory within an individual becomes feasible. Moreover, this methodological approach provides a vehicle for analyzing large datasets generated in uncontrolled clinical settings to enable better predictions of rehabilitation potential and dose-response relationships for personalized medicine.
Zhichao Yang; Mohammad H. Rafiei; Alexis Hall; Caroline Thomas; Hali A. Midtlien; Alexander Hasselbach; Hojjat Adeli; Lynne V. Gauthier. A Novel Methodology for Extracting and Evaluating Therapeutic Movements in Game-Based Motion Capture Rehabilitation Systems. Journal of Medical Systems 2018, 42, 1 -14.
AMA StyleZhichao Yang, Mohammad H. Rafiei, Alexis Hall, Caroline Thomas, Hali A. Midtlien, Alexander Hasselbach, Hojjat Adeli, Lynne V. Gauthier. A Novel Methodology for Extracting and Evaluating Therapeutic Movements in Game-Based Motion Capture Rehabilitation Systems. Journal of Medical Systems. 2018; 42 (12):1-14.
Chicago/Turabian StyleZhichao Yang; Mohammad H. Rafiei; Alexis Hall; Caroline Thomas; Hali A. Midtlien; Alexander Hasselbach; Hojjat Adeli; Lynne V. Gauthier. 2018. "A Novel Methodology for Extracting and Evaluating Therapeutic Movements in Game-Based Motion Capture Rehabilitation Systems." Journal of Medical Systems 42, no. 12: 1-14.
Artificial intelligence and expert system remains a key technology in the 21st century. Using active controllers, a structure can adaptively adjust its behaviour during dynamic loads. Such structures with self‐modifying capabilities are referred to as intelligent or smart structures. Smart structure technology has the potential to be a game changer in the structural engineering field. It promises to have enormous consequences in terms of preventing loss of life and damage to structure and their content especially for large structures with hundreds or thousands of components. A key element in successful implementation of smart active control technology is an effective control algorithm to compute the magnitudes of actual forces to be applied to the structure. In this paper, an overview of main active control methodologies for vibration control of smart civil and mechanical structures subjected to external dynamic loads is presented. The advantages and the disadvantages of different control algorithms are discussed. Finally, new trends in control algorithm research are pointed out including multiparadigm strategies, decentralized control, application of deep neural network machine learning techniques, control design for sustainability, and unification of the two fields of structural health monitoring and vibration control.
Zhijun Li; Hojjat Adeli. Control methodologies for vibration control of smart civil and mechanical structures. Expert Systems 2018, 35, e12354 .
AMA StyleZhijun Li, Hojjat Adeli. Control methodologies for vibration control of smart civil and mechanical structures. Expert Systems. 2018; 35 (6):e12354.
Chicago/Turabian StyleZhijun Li; Hojjat Adeli. 2018. "Control methodologies for vibration control of smart civil and mechanical structures." Expert Systems 35, no. 6: e12354.