Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges

Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms.To investigate advances in the use of artificial intelligence in the diagnosis,rehabilitation,and scientific examination of peripheral nerve injury,we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994–2023.We identified the following research hotspots in peripheral nerve injury and repair:(1)diagnosis,classification,and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques,such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy;(2)motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms,such as wearable devices and assisted wheelchair systems;(3)improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning,such as implantable peripheral nerve interfaces;(4)the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility,enabling them to control devices such as networked hand prostheses;(5)artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation,thereby reducing surgical risk and complications,and facilitating postoperative recovery.Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair,there are some limitations to this technology,such as the consequences of missing or imbalanced data,low data accuracy and reproducibility,and ethical issues(e.g.,privacy,data security,research transparency).Future research should address the issue of data collection,as large-scale,high-quality clinical datasets are required to establish effective artificial intelligence models.Multimodal data processing is also necessary,along with interdisciplinary collaboration,medical-industrial integration,and multicenter,large-sample clinical studies.

Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing protocols.InWSNs,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable operation.WSN data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network traversal.The mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring RPs.The unique determination of this study is the shortest path to reach RPs.As the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static sinks.In this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the MS.Both methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide coverage.In addition,a method of using MS scheduling for efficient data collection is provided.Extensive simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

Improvement of atmospheric jet-array plasma uniformity assisted by artificial neural networks

Atmospheric pressure plasma jet(APPJ)arrays have shown a potential in a wide range of applications ranging from material processing to biomedicine.In these applications,targets with complex three-dimensional structures often easily affect plasma uniformity.However,the uniformity is usually crucially important in application areas such as biomedicine,etc.In this work,the flow and electric field collaborative modulations are used to improve the uniformity of the plasma downstream.Taking a two-dimensional sloped metallic substrate with a 10°inclined angle as an example,the influences of both flow and electric field on the electron and typical active species distributions downstream are studied based on a multi-field coupling model.The electric and flow fields modulations are first separately applied to test the influence.Results show that the electric field modulation has an obvious improvement on the uniformity of plasma while the flow field modulation effect is limited.Based on such outputs,a collaborative modulation of both fields is then applied,and shows a much better effect on the uniformity.To make further advances,a basic strategy of uniformity improvement is thus acquired.To achieve the goal,an artificial neural network method with reasonable accuracy is then used to predict the correlation between plasma processing parameters and downstream uniformity properties for further improvement of the plasma uniformity.An optional scheme taking advantage of the flexibility of APPJ arrays is then developed for practical demands.

Estimating Monthly Surface Air Temperature Using MODIS LST Data and an Artificial Neural Network in the Loess Plateau, China

Air temperature(Ta)datasets with high spatial and temporal resolutions are needed in a wide range of applications,such as hydrology,ecology,agriculture,and climate change studies.Nonetheless,the density of weather station networks is insufficient,especially in sparsely populated regions,greatly limiting the accuracy of estimates of spatially distributed Ta.Due to their continuous spatial coverage,remotely sensed land surface temperature(LST)data provide the possibility of exploring spatial estimates of Ta.However,because of the complex interaction of land and climate,retrieval of Ta from the LST is still far from straightforward.The estimation accuracy varies greatly depending on the model,particularly for maximum Ta.This study estimated monthly average daily minimum temperature(Tmin),average daily maximum temperature(Tmax)and average daily mean temperature(Tmean)over the Loess Plateau in China based on Moderate Resolution Imaging Spectroradiometer(MODIS)LST data(MYD11A2)and some auxiliary data using an artificial neural network(ANN)model.The data from 2003 to 2010 were used to train the ANN models,while 2011 to 2012 weather station temperatures were used to test the trained model.The results showed that the nighttime LST and mean LST provide good estimates of Tmin and Tmean,with root mean square errors(RMSEs)of 1.04℃ and 1.01℃,respectively.Moreover,the best RMSE of Tmax estimation was 1.27℃.Compared with the other two published Ta gridded datasets,the produced 1 km×1 km dataset accurately captured both the temporal and spatial patterns of Ta.The RMSE of Tmin estimation was more sensitive to elevation,while that of Tmax was more sensitive to month.Except for land cover type as the input variable,which reduced the RMSE by approximately 0.01℃,the other vegetation-related variables did not improve the performance of the model.The results of this study indicated that ANN,a type of machine learning method,is effective for long-term and large-scale Ta estimation.

Multi-style Chord Music Generation Based on Artificial Neural Network

With the continuous development of deep learning and artificial neural networks(ANNs), algorithmic composition has gradually become a hot research field. In order to solve the music-style problem in generating chord music, a multi-style chord music generation(MSCMG) network is proposed based on the previous ANN for creation. A music-style extraction module and a style extractor are added by the network on the original basis;the music-style extraction module divides the entire music content into two parts, namely the music-style information Mstyleand the music content information Mcontent. The style extractor removes the music-style information entangled in the music content information. The similarity of music generated by different models is compared in this paper. It is also evaluated whether the model can learn music composition rules from the database. Through experiments, it is found that the model proposed in this paper can generate music works in the expected style. Compared with the long short term memory(LSTM) network, the MSCMG network has a certain improvement in the performance of music styles.

An Efficient and Robust Fall Detection System Using Wireless Gait Analysis Sensor with Artificial Neural Network (ANN) and Support Vector Machine (SVM) Algorithms

In this work, a total of 322 tests were taken on young volunteers by performing 10 different falls, 6 different Activities of Daily Living (ADL) and 7 Dynamic Gait Index (DGI) tests using a custom-designed Wireless Gait Analysis Sensor (WGAS). In order to perform automatic fall detection, we used Back Propagation Artificial Neural Network (BP-ANN) and Support Vector Machine (SVM) based on the 6 features extracted from the raw data. The WGAS, which includes a tri-axial accelerometer, 2 gyroscopes, and a MSP430 microcontroller, is worn by the subjects at either T4 (at back) or as a belt-clip in front of the waist during the various tests. The raw data is wirelessly transmitted from the WGAS to a near-by PC for real-time fall classification. The BP ANN is optimized by varying the training, testing and validation data sets and training the network with different learning schemes. SVM is optimized by using three different kernels and selecting the kernel for best classification rate. The overall accuracy of BP ANN is obtained as 98.20% with LM and RPROP training from the T4 data, while from the data taken at the belt, we achieved 98.70% with LM and SCG learning. The overall accuracy using SVM was 98.80% and 98.71% with RBF kernel from the T4 and belt position data, respectively.

Optimization of the Conceptual Model of Green-Ampt Using Artificial Neural Network Model (ANN) and WMS to Estimate Infiltration Rate of Soil (Case Study: Kakasharaf Watershed, Khorram Abad, Iran)

Determination of the infiltration rate in a watershed is not easy and in empirical and theoretical point of view, it is important to access average value of infiltration. Infiltration models has main role in managing water sources. Therefore different types of models with various degrees of complexity were developed to reach this aim. Most of the estimating methods of soil infiltration are expensive and time consuming and these methods estimate infiltration with hypothesis of zero slope. One of the conceptual and physical models for estimating soil infiltration is Green-Ampt model which is similar to Richard model. This model uses slope factor in estimating infiltration and this is the power point of Green-Ampt model. In this research the empirical model of Green-Ampt was optimized with integrating artificial neural network model (ANN) and a model of geographical information system WMS to estimate the infiltration in Kakasharaf watershed. Results of the comparison between the output of this method and real value of infiltration in region (through multiple cylinders) showed that this method can estimate the infiltration rate of Kakasharaf watershed with low error and acceptable accuracy (Nash-Sutcliff performance coefficient 0.821, square error 0.216, correlation coefficient 0.905 and model error 0.024).

Predicting pollutant removal in constructed wetlands using artificial neural networks(ANNs)

Growth in urban population,urbanisation,and economic development has increased the demand for water,especially in water-scarce regions.Therefore,sustainable approaches to water management are needed to cope with the effects of the urbanisation on the water environment.This study aimed to design novel configurations of tidal-flow vertical subsurface flow constructed wetlands(VFCWs)for treating urban stormwater.A series of laboratory experiments were conducted with semi-synthetic influent stormwater to examine the effects of the design and operation variables on the performance of the VFCWs and to identify optimal design and operational strategies,as well as maintenance requirements.The results show that the VFCWs can significantly reduce pollutants in urban stormwater,and that pollutant removal was related to specific VFCW designs.Models based on the artificial neural network(ANN)method were built using inputs derived from data exploratory techniques,such as analysis of variance(ANOVA)and principal component analysis(PCA).It was found that PCA reduced the dimensionality of input variables obtained from different experimental design conditions.The results show a satisfactory generalisation for predicting nitrogen and phosphorus removal with fewer variable inputs,indicating that monitoring costs and time can be reduced.

基于ANN和XGB算法的锈蚀钢筋混凝土高温粘结强度预测方法

为准确评估锈蚀钢筋混凝土(CRC)结构在突发火灾下的结构承载力,锈蚀钢筋混凝土高温粘结强度的统一预测方法研究亟待开展。然而,粘结退化机理复杂,粘结因素众多,实验方法不能考虑所有粘结因素的相关复杂关系的影响。在现有大量试验数据的基础上,采用机器学习方法可以有效地通过数据建立输入和输出特征之间的回归关系。该文利用ANN和XGB两种机器学习算法建立了一个统一的锈蚀钢筋混凝土高温粘结强度预测模型。基于612组高温锈蚀钢筋混凝土的试验研究数据,进行模型训练和测试。结果表明:ML模型的预测结果与实验结果十分吻合。此外,针对机器学习算法本身存在的黑盒子问题,使用SHAP方法来解决锈蚀钢筋混凝土高温粘结强度预测过程中的模型可解释性问题。同时,还将ML模型的计算结果与三种理论计算公式的结果进行了比较,结果表明:ML模型具有明显的优势。新构建的混合机器学习模型很有可能成为准确评估CRC结构经受高温后的损伤程度问题的新选择。

EFFECT OF COLD WORKING ON THE AGING PROPERTIES OF Cu-Cr-Zr-Mg ALLOY BY ARTIFICIAL NEURAL NETWORK

A developmental research has been carried out to deal with the high performance of Cu-Cr-Zr-Mg lead frame alloy by artificial neural network (ANN). Using the cold working to assist in the aging hardening can improve the the hardness and electrical conductivity properties of Cu-Cr-Zr-Mg lead frame alloy. This paper studies the effect of different extent of cold working on the aging properties by a supervised ANN to model the non-linear relationship between processing parameters and the properties. The back-propagation (BP) training algorithm is improved by Levenberg-Marquardt algorithm. A basic repository on the domain knowledge of cold worked aging processes is established via sufficient data mining by the network. The predicted values of the ANN coincide well with the tested data. So an important foundation has been laid for prediction and optimum controlling the rolling and aging properties of Cu-Cr-Zr-Mg alloy.

A robust behavior of Feed Forward Back propagation algorithm of Artificial Neural Networks in the application of vertical electrical sounding data inversion

The applications of intelligent techniques have increased exponentially in recent days to study most of the non-linear parameters. In particular, the behavior of earth resembles the non- linearity applications. An efficient tool is needed for the interpretation of geophysical parameters to study the subsurface of the earth. Artificial Neural Networks （ANN） perform certain tasks if the structure of the network is modified accordingly for the purpose it has been used. The three most robust networks were taken and comparatively analyzed for their performance to choose the appropriate network. The single- layer feed-forward neural network with the back propagation algorithm is chosen as one of the well- suited networks after comparing the results. Initially, certain synthetic data sets of all three-layer curves have been taken tk^r training the network, and the network is validated by the field datasets collected from Tuticorin Coastal Region （78°7＇30＂E and 8°48＇45＂N）, Tamil Nadu, India. The interpretation has been done successfully using the corresponding learning algorithm in the present study. With proper training of back propagation networks, it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data concerning the synthetic data trained earlier in the appropriate network. The network is trained with more Vertical Electrical Sounding （VES） data, and this trained network is demon- strated by the field data. Groundwater table depth also has been modeled.

Prediction of roadheaders' performance using artificial neural network approaches (MLP and KOSFM)

A pplication o f m echanical excavators is one o f th e m o st com m only used excavation m eth o d s because itcan bring th e p ro ject m ore productivity, accuracy and safety. A m ong th e m echanical excavators, roadhead ers are m echanical m iners w h ich have b een extensively u se d in tu n n elin g , m ining an d civil indu stries. Perform ance pred ictio n is an im p o rta n t issue for successful ro a d h e a d e r application andgenerally deals w ith m achine selection, p ro d u ctio n rate an d b it consu m p tio n . The m ain aim o f thisresearch is to investigate th e c u ttin g p erfo rm an ce （in stan tan eo u s c u ttin g rates （ICRs）） o f m ed iu m -d u tyro ad h ead ers by using artificial neural n etw o rk （ANN） approach. T here are d ifferent categories forANNs, b u t based o n train in g alg o rith m th e re are tw o m ain k in d s： supervised and u n su p erv ised . Them u lti-lay er p ercep tro n （MLP） an d K ohonen self-organizing feature m ap （KSOFM） are th e m o st w idelyused neu ral netw o rk s for supervised an d u n su p erv ised ones, respectively. For gaining this goal, ad atab ase w as prim arily provided from ro ad h e a d e rs＇ p erfo rm an ce an d geom echanical characteristics o frock form ations in tu n n els and d rift galleries in Tabas coal m ine, th e larg est an d th e only fullymech an ized coal m ine in Iran. T hen th e datab ase w as analyzed in o rd e r to yield th e m ost im p o rtan tfactor for ICR by using relatively im p o rta n t factor in w hich G arson eq u atio n w as utilized. The MLPn etw o rk w as train ed by 3 in p u t p ara m e te rs including rock m ass pro p erties, rock quality d esignation（RQD）, in tact rock p ro p erties such as uniaxial com pressive stre n g th （UCS） an d Brazilian ten sile stren g th（BTS）, and o n e o u tp u t p a ra m e te r （ICR）. In o rd e r to have m ore v alidation o n MLP o u tp u ts, KSOFM visualizationw as applied. The m ean square e rro r （MSE） an d regression coefficient （R ） o f MLP w e re found tobe 5.49 an d 0.97, respectively. M oreover, KSOFM n etw o rk has a m ap size o f 8 x 5 and final qu an tizatio nan d topographic erro rs w e re 0.383 an d 0.032, respectively. The results show th a t MLP neural n etw orkshave a strong capability to p red ict an d ev alu ate th e perfo rm an ce o f m ed iu m -d u ty ro ad h ead ers in coalm easu re rocks. Furtherm ore, it is concluded th a t KSOFM neural n etw o rk is an efficient w ay for u n d e rstand in g system beh av io r an d know ledge extraction. Finally, it is indicated th a t UCS has m ore influenceo n ICR b y applying th e b e st train ed MLP n etw o rk w eig h ts in G arson eq u atio n w h ich is also confirm ed byKSOFM.

Prediction of TBM jamming risk in squeezing grounds using Bayesian and artificial neural networks

This study presents an application of artificial neural network(ANN)and Bayesian network(BN)for evaluation of jamming risk of the shielded tunnel boring machines(TBMs)in adverse ground conditions such as squeezing grounds.The analysis is based on database of tunneling cases by numerical modeling to evaluate the ground convergence and possibility of machine entrapment.The results of initial numerical analysis were verified in comparison with some case studies.A dataset was established by performing additional numerical modeling of various scenarios based on variation of the most critical parameters affecting shield jamming.This includes compressive strength and deformation modulus of rock mass,tunnel radius,shield length,shield thickness,in situ stresses,depth of over-excavation,and skin friction between shield and rock.Using the dataset,an ANN was trained to predict the contact pressures from a series of ground properties and machine parameters.Furthermore,the continuous and discretized BNs were used to analyze the risk of shield jamming.The results of these two different BN methods are compared to the field observations and summarized in this paper.The developed risk models can estimate the required thrust force in both cases.The BN models can also be used in the cases with incomplete geological and geomechanical properties.

Recovery of saturated signal waveform acquired from high-energy particles with artificial neural networks

Artificial neural networks(ANNs)are a core component of artificial intelligence and are frequently used in machine learning.In this report,we investigate the use of ANNs to recover the saturated signals acquired in highenergy particle and nuclear physics experiments.The inherent properties of the detector and hardware imply that particles with relatively high energies probably often generate saturated signals.Usually,these saturated signals are discarded during data processing,and therefore,some useful information is lost.Thus,it is worth restoring the saturated signals to their normal form.The mapping from a saturated signal waveform to a normal signal waveform constitutes a regression problem.Given that the scintillator and collection usually do not form a linear system,typical regression methods such as multi-parameter fitting are not immediately applicable.One important advantage of ANNs is their capability to process nonlinear regression problems.To recover the saturated signal,three typical ANNs were tested including backpropagation(BP),simple recurrent(Elman),and generalized radial basis function(GRBF)neural networks(NNs).They represent a basic network structure,a network structure with feedback,and a network structure with a kernel function,respectively.The saturated waveforms were produced mainly by the environmental gamma in a liquid scintillation detector for the China Dark Matter Detection Experiment(CDEX).The training and test data sets consisted of 6000 and 3000 recordings of background radiation,respectively,in which saturation was simulated by truncating each waveform at 40%of the maximum signal.The results show that the GBRF-NN performed best as measured using a Chi-squared test to compare the original and reconstructed signals in the region in which saturation was simulated.A comparison of the original and reconstructed signals in this region shows that the GBRF neural network produced the best performance.This ANN demonstrates a powerful efficacy in terms of solving the saturation recovery problem.The proposed method outlines new ideas and possibilities for the recovery of saturated signals in high-energy particle and nuclear physics experiments.This study also illustrates an innovative application of machine learning in the analysis of experimental data in particle physics.

Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results

Rolling dynamic compaction(RDC),which involves the towing of a noncircular module,is now widespread and accepted among many other soil compaction methods.However,to date,there is no accurate method for reliable prediction of the densification of soil and the extent of ground improvement by means of RDC.This study presents the application of artificial neural networks(ANNs) for a priori prediction of the effectiveness of RDC.The models are trained with in situ dynamic cone penetration(DCP) test data obtained from previous civil projects associated with the 4-sided impact roller.The predictions from the ANN models are in good agreement with the measured field data,as indicated by the model correlation coefficient of approximately 0.8.It is concluded that the ANN models developed in this study can be successfully employed to provide more accurate prediction of the performance of the RDC on a range of soil types.

Structural reliability analysis using enhanced cuckoo search algorithm and artificial neural network

The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.

Science Letters:Serum protein fingerprinting coupled with artificial neural network distinguishes glioma from healthy population or brain benign tumor

To screen and evaluate protein biomarkers for the detection of gliomas (Astrocytoma grade Ⅰ-Ⅳ) from healthy individuals and gliomas from brain benign tumors by using surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS) coupled with an artificial neural network (ANN) algorithm. SELDI-TOF-MS protein fingerprinting of serum from 105 brain tumor patients and healthy individuals, included 28 patients with glioma (Astrocytoma Ⅰ-Ⅳ), 37 patients with brain benign tumor, and 40 age-matched healthy individuals. Two thirds of the total samples of every compared pair as training set were used to set up discriminating patterns, and one third of total samples of every compared pair as test set were used to cross-validate; simultaneously, discriminate-cluster analysis derived SPSS 10.0 software was used to compare Astrocytoma grade Ⅰ-Ⅱ with grade Ⅲ-Ⅳ ones. An accuracy of 95.7%, sensitivity of 88.9%, specificity of 100%, positive predictive value of 90% and negative predictive value of 100% were obtained in a blinded test set comparing gliomas patients with healthy individuals; an accuracy of 86.4%, sensitivity of 88.9%, specificity of 84.6%, positive predictive value of 90% and negative predictive value of 85.7% were obtained when patient's gliomas was compared with benign brain tumor. Total accuracy of 85.7%, accuracy of grade Ⅰ-Ⅱ Astrocytoma was 86.7%, accuracy ofⅢ-Ⅳ Astrocytoma was 84.6% were obtained when grade Ⅰ-Ⅱ Astrocytoma was compared with grade Ⅲ-Ⅳ ones (discriminant analysis). SELDI-TOF-MS combined with bioinformatics tools, could greatly facilitate the discovery of better biomarkers. The high sensitivity and specificity achieved by the use of selected biomarkers showed great potential application for the discrimination of gliomas patients from healthy individuals and glioma from brain benign tumors.

Application of Artificial Neural Network to Predicting Hardenability of Gear Steel

The prediction of the hardenability and chemical composition of gear steel was studied using artificial neural networks. A software was used to quantitatively forecast the hardenability by its chemical composition or the chemical composition by its hardenability. The prediction result is more precise than that obtained from the traditional method based on the simple mathematical regression model.

Artificial neural network optimized by differential evolution for predicting diameters of jet grouted columns

A novel and effective artificial neural network(ANN) optimized using differential evolution(DE) is first introduced to provide a robust and reliable forecasting of jet grouted column diameters.The proposed computational method adopts the DE algorithm to tackle the difficulties in the training and performance of neural networks and optimize the four quintessential hyper-parameters(i.e.the epoch size,the number of neurons in a hidden layer,the number of hidden layers,and the regularization parameter) that govern the neural network efficacy.This approach is further enhanced by a stochastic gradient optimization algorithm to allow ’expensive’ computation efforts.The ANN-DE is first trained using a prepared jet grouting dataset,then verified and compared with the prevalent machine learning tools,i.e.neural networks and support vector machine(SVM).The results show that,the ANN-DE outperforms the existing methods for predicting the diameter of jet grouting columns since it well balances training efficiency and model performance.Specifically,the ANN-DE achieved root mean square error(RMSE)values of 0.90603 and 0.92813 for the training and testing phases,respectively.The corresponding values were 0.8905 and 0.9006 for the optimized ANN,then,0.87569 and 0.89968 for the optimized SVM,respectively.The proposed paradigm is bound to be useful for solving various geotechnical engineering problems regardless of multi-dimension and nonlinearity.