Seismic evaluation of frequency influence and resonant behavior for lead rubber bearing isolated rigid rectangular liquid tanks

The seismic behavior of a partially filled rigid rectangular liquid tank is investigated under short-and longduration ground motions.A finite element model is developed to analyze the liquid domain by using four-noded quadrilateral elements.The competency of the model is verified with the available results.Parametric studies are conducted for the dynamic parameters of the base-isolated tank,using a lead rubber bearing to evaluate the optimum damping and time period of the isolator.The application of base isolation has reduced the total and impulsive hydrodynamic components of pressure by 80 to 90 percent,and base shear by 15 to 95 percent,depending upon the frequency content and duration of the considered earthquakes.The sloshing amplitude of the base-isolated tank is reduced by 18 to 94 percent for most of the short-duration earthquakes,while it is increased by 17 to 60 percent for the majority of the long-duration earthquakes.Furthermore,resonance studies are carried out through a long-duration harmonic excitation to obtain the dynamic behavior of non-isolated and isolated tanks,using a nonlinear sloshing model.The seismic responses of the base-isolated tank are obtained as higher when the excitation frequency matches the fundamental sloshing frequency rather than the isolator frequency.

Synthesis of plant-based biogenic jarosite nanoparticles using Azadirachta indica and Eucalyptus gunni leaf extracts and its application in Fenton degradation of dicamba

Bio-jarosite,an iron mineral synthesized biologically using bacteria,is a substitute for iron catalysts in the Fenton oxidation of organic pollutants.Iron nanocatalysts have been widely used as Fenton catalysts because they have a larger surface area than ordinary catalysts,are highly recyclable,and can be treated efficiently.This study aimed to explore the catalytic properties of bio-jarosite iron nanoparticles syn-thesized with green methods using two distinct plant species:Azadirachta indica and Eucalyptus gunni.The focus was on the degradation of dicamba via Fenton oxidation.The synthesized nanoparticles exhibited different particle size,shape,surface area,and chemical composition characteristics.Both particles were effective in removing dicamba,with removal efficiencies of 96.8%for A.indica bio-jarosite iron nano-particles(ABFeNPs)and 93.0%for E.gunni bio-jarosite iron nanoparticles(EBFeNPs)within 120 min of treatment.Increasing the catalyst dosage by 0.1 g/L resulted in 7.6%and 43.0%increases in the dicamba removal efficiency for EBFeNPs and ABFeNPs with rate constants of 0.025 min^(-1) and 0.023 min^(-1),respectively,confrming their catalytic roles.Additionally,the high efficiency of both catalysts was demonstrated through five consecutive cycles of linear pseudo-first-order Fenton oxidation reactions.

An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials

This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal performance.The EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was built.To train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was collected.Based on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive precision.Experimental consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)phases.The outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.

Spatial and temporal analysis of urban heat island effect over Tiruchirappalli city using geospatial techniques

Alterations made to the natural ground surface and the anthropogenic activity elevate the surface and air temperature in the urban areas compared with the surrounding rural areas,known as urban heat island effect.Thermal remote sensors measure the radiation emitted by ground objects,which can be used to estimate the land surface temperature and are beneficial for studying urban heat island effects.The present study investigates the spatial and temporal variations in the effects of urban heat island over Tiruchirappalli city in India during the summer and winter seasons.The study also identifies hot spots and cold spots within the study area.In this study,a significant land surface temperature difference was observed between the urban and rural areas,predominantly at night,indicating the presence of urban heat island at night.These diurnal land surface temperature fluctuations are also detected seasonally,with a relatively higher temperature intensity during the summer.The trend line analysis shows that the mean land surface temperature of the study area is increasing at a rate of 0.166 K/decade with p less than 0.01.By using the spatial autocorrelation method with the urban heat island index as the key parameter,hot spots with a 99 percent confidence level and a 95 percent confidence level were found within the urban area.A hot spot with 95 and 90 percent confidence level was identified outside the urban area.This spike in temperature for a particular region in the rural area is due to industry and the associated built-up area.The study also identified cold spots with a 90 percent confidence level within the rural area.However,cold spots with a 95 and 99 percent confidence level were not identified within the study area.

Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling

Settlement prediction of geosynthetic-reinforced soil(GRS)abutments under service loading conditions is an arduous and challenging task for practicing geotechnical/civil engineers.Hence,in this paper,a novel hybrid artificial intelligence(AI)-based model was developed by the combination of artificial neural network(ANN)and Harris hawks’optimisation(HHO),that is,ANN-HHO,to predict the settlement of the GRS abutments.Five other robust intelligent models such as support vector regression(SVR),Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimisation regression(SMOR),and least-median square regression(LMSR)were constructed and compared to the ANN-HHO model.The predictive strength,relalibility and robustness of the model were evaluated based on rigorous statistical testing,ranking criteria,multi-criteria approach,uncertainity analysis and sensitivity analysis(SA).Moreover,the predictive veracity of the model was also substantiated against several large-scale independent experimental studies on GRS abutments reported in the scientific literature.The acquired findings demonstrated that the ANN-HHO model predicted the settlement of GRS abutments with reasonable accuracy and yielded superior performance in comparison to counterpart models.Therefore,it becomes one of predictive tools employed by geotechnical/civil engineers in preliminary decision-making when investigating the in-service performance of GRS abutments.Finally,the model has been converted into a simple mathematical formulation for easy hand calculations,and it is proved cost-effective and less time-consuming in comparison to experimental tests and numerical simulations.

Application of soft computing techniques for shallow foundation reliability in geotechnical engineering

This research focuses on the application of three soft computing techniques including Minimax Probability Machine Regression(MPMR),Particle Swarm Optimization based Artificial Neural Network(ANN-PSO)and Particle Swarm Optimization based Adaptive Network Fuzzy Inference System(ANFIS-PSO)to study the shallow foundation reliability based on settlement criteria.Soil is a heterogeneous medium and the involvement of its attributes for geotechnical behaviour in soil-foundation system makes the prediction of settlement of shallow a complex engineering problem.This study explores the feasibility of soft computing techniques against the deterministic approach.The settlement of shallow foundation depends on the parametersγ(unit weight),e0(void ratio)and CC(compression index).These soil parameters are taken as input variables while the settlement of shallow foundation as output.To assess the performance of models,different performance indices i.e.RMSE,VAF,R^2,Bias Factor,MAPE,LMI,U(95),RSR,NS,RPD,etc.were used.From the analysis of results,it was found that MPMR model outperformed PSO-ANFIS and PSO-ANN.Therefore,MPMR can be used as a reliable soft computing technique for non-linear problems for settlement of shallow foundations on soils.

Predicting Rock Burst in Underground Engineering Leveraging a Novel Metaheuristic-Based LightGBM Model

Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human safety.These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock,leading to severe seismic events and structural damage.Therefore,the development of reliable prediction models for rock bursts is paramount to mitigating these hazards.This study aims to propose a tree-based model—a Light Gradient Boosting Machine(LightGBM)—to predict the intensity of rock bursts in underground engineering.322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset,which serves to train the LightGBMmodel.Two population-basedmetaheuristic algorithms are used to optimize the hyperparameters of the LightGBM model.Finally,the sensitivity analysis is used to identify the predominant factors that may incur the occurrence of rock bursts.The results show that the population-based metaheuristic algorithms have a good ability to search out the optimal hyperparameters of the LightGBM model.The developed LightGBM model yields promising performance in predicting the intensity of rock bursts,with which accuracy on training and testing sets are 0.972 and 0.944,respectively.The sensitivity analysis discloses that the risk of occurring rock burst is significantly sensitive to three factors:uniaxial compressive strength(σc),stress concentration factor(SCF),and elastic strain energy index(Wet).Moreover,this study clarifies the particular impact of these three factors on the intensity of rock bursts through the partial dependence plot.

Numerical analysis of surface subsidence in asymmetric parallel highway tunnels

Tunnelling related hazards are very common in the Himalayan terrain and a number of such instances have been reported. Several twin tunnels are being planned for transportation purposes which will require good understanding for prediction of tunnel deformation and surface settlement during the engineering life of the structure. The deformational behaviour, design of sequential excavation and support of any jointed rock mass are challenging during underground construction. We have raised several commonly assumed issues while performing stability analysis of underground opening at shallow depth. For this purpose, Kainchi-mod Nerchowck twin tunnels(Himachal Pradesh, India) are taken for in-depth analysis of the stability of two asymmetric tunnels to address the influence of topography, twin tunnel dimension and geometry. The host rock encountered during excavation is composed mainly of moderately to highly jointed grey sandstone, maroon sandstone and siltstones. In contrast to equidimensional tunnels where the maximum subsidence is observed vertically above the centreline of the tunnel, the result from the present study shows shifting of the maximum subsidence away from the tunnel centreline. The maximum subsidence of 0.99 mm is observed at 4.54 m left to the escape tunnel centreline whereas the maximum subsidence of 3.14 mm is observed at 8.89 m right to the main tunnel centreline. This shifting clearly indicates the influence of undulating topography and inequidimensional noncircular tunnel.

Fatigue reliability analysis of fixed offshore structures:A first passage problem approach

This paper describes a methodology for computation of reliability of members of fixed offshore platform structures, with respect to fatigue. Failure criteria were formulated using fracture mechanics principle. The problem is coined as a “first passage problem”. The method was illustrated through application to a typical plane frame structure. The fatigue reliability degradation curve established can be used for planning in-service inspection of offshore platforms. A very limited parametric study was carried out to obtain insight into the effect of important variables on the fatigue reliability.

A numerical modelling approach to assess the behaviour of underground cavern subjected to blast loads

The paper gives an insight into the behaviour of large underground caverns which are subjected to blast loads. Caverns are generally constructed in hard rock formation which compels us to use blasting methods for the excavation works. Comparative study was done between models with intact rock mass and discontinuities to assess the stability of cavern as a result of blast loads. Numerical modelling was performed with 3 dimensional distinct element code(3 DEC) to analyse the performance of cavern walls in terms of displacement and to compute peak particle velocities(PPV) both around the cavern periphery and at surface of models. Results showed that the velocity wave with higher frequency exhibited large displacements around the periphery of cavern. Computation of PPV showed that model with horizontal joint sets showed lower PPV in comparison to model with intact rock mass. PPV values were also analysed on the surface for model consisting vertical joints spaced at 4 m intervals. Comparative study of PPV on surface vertically above the blast location between models with horizontal joints spaced at 4 m and vertical joints at 4 m intervals were conducted. Results depicted higher magnitudes of PPV for model with vertical joints in comparison to model with horizontal joints.

Finite element analysis of dynamic stability of skeletal structures under periodic loading

This paper addresses the dynamic stability problem of columns and frames subjected to axially applied periodic loads. Such a structure can become unstable under certain combinations of amplitudes and frequencies of the imposed load acting on its columns/beams. These are usually shown in the form of plots which describe regions of instability. The finite element method (FEM) is used in this work to analyse dynamic stability problems of columns. Two-noded beam elements are used for this purpose. The periodic loading is decomposed into various harmonics using Fourier series expansion. Computer codes in C++ using object oriented concepts are developed to determine the stability regions of columns subjected to periodic loading. A number of nu-merical examples are presented to illustrate the working of the program. The direct integration of the equations of motions of the discretised system is carried out using Newmark’s method to verify the results.

Earthquake response of asymmetric building with MR damper

Plan asymmetric buildings are very susceptible to earthquake induced damage due to lateral torsional coupling, and the corners of these systems suffer heavy damage during earthquakes. Therefore, it is important to investigate the seismic behavior of an asymmetric plan building with MR dampers. In this study, the effectiveness of MR damper-based control systems has been investigated for seismic hazard mitigation of a plan asymmetric building. Furthermore, the infl uence of the building parameters and damper command voltage on the control performance is examined through parametric study. The building parameters chosen are eccentricity ratio and frequency ratio. The results show that the MR damper-based control systems are effective for plan asymmetric systems.

Development and subassemblage cyclic testing of hybrid buckling-restrained steel braces

Buckling-restrained braced frames(BRBFs) are vulnerable to relatively higher post-earthquake residual drifts under high intensity ground shakings. This is primarily due to the low axial elastic and post-elastic stiffness of bucklingrestrained braces(BRBs) satisfying the design force demand requirements. In the present study, a hybrid buckling restrained bracing system consisting of a short yielding core length BRB component and a conventional buckling-type brace component connected in series has been developed with an aim to increase the axial stiffness of braces. This study is focused on the experimental investigation of six hybrid bucking restrained braces(HBRBs) to investigate their overall behavior, loadresisting capacity, strength-adjustment factors and energy dissipation potential. The main parameters varied are the crosssectional area, the yielding length of core elements as well as the detailing of buckling-restraining system of short yielding core length BRBs. Test results showed that the HBRBs with yielding core length in the range of 30% of work-point to workpoint lengths withstood an axial strain of 6% without any instability and can deliver stable and balanced hysteretic response and excellent energy dissipation under reversed cyclic loading conditions.

Precipitation-runoff simulation for a Himalayan River Basin, India using artificial neural network algorithms

In the Himalayan regions, precipitation-runoff relationships are amongst the most complex hydrological phenomena, due to varying topography and basin characteristics. In this study, different artificial neural networks （ANNs） algorithms were used to simulate daily nmoff at three discharge measuring sites in the Himalayan Kosi River Basin, India, using various combinations of precipitation-runoff data as input variables. The data used for this study was collected for the monsoon period （June to Oc- tober） during the years of 2005 to 2009. ANNs were trained using different training algorithms, leaming rates, length of data and number of hidden neurons. A comprehensive multi-criteria validation test for precipitation-runoff modeling has been un- dertaken to evaluate model performance and test its validity for generating scenarios. Global statistics have demonstrated that the multilayer perceptron with three hidden layers （MLP-3） is the best ANN for basin comparisons with other MLP networks and Radial Basis Functions （RBF）. Furthermore, non-parametric tests also illustrate that the MLP-3 network is the best net- work to reproduce the mean and variance of observed runoff. The performance of ANNs was demonstrated for flows during the monsoon season, having different soil moisture conditions during period from June to October.

Time-series analysis of monthly rainfall data for the Mahanadi River Basin, India

Time series analysis has two goals, modeling random mechanisms and predicting future series using historical data. In the present work, a uni-variate time series autoregressive integrated moving average （ARIMA） model has been developed for （a） simulating and forecasting mean rainfall, obtained using Theissen weights; over the Mahanadi River Basin in India, and （b） simula^ag and forecasting mean rainfall at 38 rain-gauge stations in district towns across the basin. For the analysis, monthly rainfall data of each district town for the years 1901-2002 （102 years） were used. Theissen weights were obtained over the basin and mean monthly rainfall was estimated. The trend and seasonality observed in ACF and PACF plots of rainfall data were removed using power transformation （a=0.5） and first order seasonal differencing prior to the development of the ARIMA model. Interestingly, the AR1MA model （1,0,0）（0,1,1）12 developed here was found to be most suitable for simulating and forecasting mean rainfall over the Mahanadi River Basin and for all 38 district town rain-gauge stations, separately. The Akaike Information Criterion （AIC）, good- ness of fit （Chi-square）, R2 （coefficient of determination）, MSE （mean square error） and MAE （mea absolute error） were used to test the validity and applicability of the developed ARIMA model at different stages. This model is considered appropriate to forecast the monthly rainfall for the upcoming 12 years in each district town to assist decision makers and policy makers establish priorities for water demand, storage, distribution, and disaster management.

Laboratory performance of stone matrix asphalt mixtures with two aggregate gradations

Stone matrix asphalt （SMA） is a gap-graded bituminous mixture which can be used in surface layer of high volume pavements. The mixture has higher concen- trations of coarse aggregates, providing strength and rut resistance to the mixture, and higher asphalt content giving durability. There must be a proper stone-to-stone contact between the coarse aggregates of SMA, and hence aggre- gate gradation is an important factor in this mixture. In the current study, two aggregate gradations, with nominal maximum aggregate sizes （NMAS） 16 and 13 mm were adopted to prepare SMA mixtures and their laboratory performances were compared. Polymer-modified bitumen （PMB） was used as the binder material and no stabilising additive was used, since drain down was within permissible limits for both mixtures with PMB. Conventional cylin- drical specimens were prepared in superpave gyratory compactor with bitumen contents 5.0 %, 5.5 %, 6.0 %, 6.5 % and 7.0 % by weight of aggregates, and volumetric and Marshall properties were determined. Tensile strength, behaviour to repeated loading etc. were checked for cylindrical specimens prepared at optimum bitumen con- tent, whereas specially prepared slab specimens were used to check the rutting resistance of SMA mixtures. From the laboratory study, it was observed that, out of the two SMA mixtures, the one with NMAS 16 mm performed better compared to the other. These improved properties may be attributed towards the larger coarse aggregate sizes in the mixture.

A Model Study on Accelerated Consolidation of Coir Reinforced Lateritic Lithomarge Soil Blends with Vertical Sand Drains for Pavement Foundations

Sub-grade soils of lateritic origin encountered in the construction of highway embankments in various regions of India, often comprise intrusions of soft lithomargic soils that result in large settlements during constructions, and differential settlements at later stages. This necessitates the use of appropriate soil improvement techniques to improve the load-carrying capacity of pavements. This work deals with accelerated consolidation of un-reinforced and coir-rein- forced lateritic lithomargic soil blends, provided with three vertical sand drains. The load-settlement characteristics were studied for various preloads ranging from 50 kg (0.0013 N/mm2) to 500 kg (0.013N/mm2) on soil specimens prepared in circular ferro-cement moulds. It was observed that at lower preloads up to 200kg, across the blends, the relative increase in consolidation (Rct) for randomly reinforced soil with vertical drains was sig-nificantly higher than that of un-reinforced soil without vertical drains, with an average value of 124.8%. Also, the Rct for un-reinforced soil with vertical drains was quite higher than that of un-reinforced soil without vertical drains, with an average value of 103.9%. In the case of higher preloads, the Rct values for randomly reinforced soil with vertical drains were moderate with an average value of 30.88%, while the same for un-reinforced soil with vertical drains was about 20.4%. The aspect-ratio of coir fibers used was 1:275.

Performance evaluation of cement-stabilized pond ash-rice husk ashclay mixture as a highway construction material

This paper reports the results of an investigation carried out on clay soil stabilized with pond ash(PA),rice husk ash(RHA) and cement. Modified Proctor compaction tests were performed in order to investigate the compaction behavior of clay, and California bearing ratio(CBR) tests were performed to determine the strength characteristics of clay. For evaluation purpose, the specimens containing different amounts of admixtures were prepared. Clay was replaced with PA and RHA at a dosage of 30%e45% and5%e20%, respectively. The influence of stabilizer types and dosages on mechanical properties of clay was evaluated. In order to study the surface morphology and crystallization characteristics of the soil samples, scanning electron microscopy(SEM) and X-ray diffraction(XRD) analyses were carried out,respectively. The results obtained indicated a decrease in the maximum dry density(MDD) and a simultaneous increase in the optimum moisture content(OMC) with the addition of PA and RHA.Multiple linear regression analysis(MLRA) showed that the predicted values of CBR tests are in good agreement with the experimental values. Developed stabilized soil mixtures showed satisfactory strength and can be used for construction of embankments and stabilization of sub-grade soil. The use of locally available soils, PA, RHA, and cement in the production of stabilized soils for such applications can provide sustainability for the local construction industry.

Reliability Analysis of Piled Raft Foundation Using a Novel Hybrid Approach of ANN and Equilibrium Optimizer

In many civil engineering projects,Piled Raft Foundations(PRFs)are usually preferred where the incoming load fromthe superstructures is very high.In geotechnical engineering practice,the settlement of soil layers is a critical issue for the serviceability of the structures.Thus,assessment of risk associated with the structures corresponding to the maximum allowable settlement of soils needs to be carried out in the design phase.In this study,reliability analysis of PRF based on settlement criteria is performed using a high-performance hybrid soft computing model.The new approach is an integration of the artificial neural network(ANN)and a recently developed meta-heuristic algorithm called equilibrium optimizer(EO).The concept of reliability index was used to explore the feasibility of a newly constructed hybrid model of ANN and EO(i.e.,ANN-EO)against the conventional approach of calculating the probability of failure of PRF.Experimental results show that the proposed ANN-EO attained the most accurate prediction with R^(2)=0.9914 and RMSE=0.0518 in the testing phase,which are significantly better than those obtained from conventional ANN,multivariate adaptive regression splines,and genetic programming,including the ANNoptimized with particle swarmoptimization developed in this study.Based on the experimental results of different settlement values,the newly constructedANN-EOis very potential to analyze the risk associatedwith civil engineering structures.Also,the present study would significantly contribute to the knowledge pool of reliability studies related to piled raft systems because the works of literature on reliability analysis of piled raft systems are relatively scarce.

Pedestrian perception-based level-of-service model at signalized intersection crosswalks

Pedestrian level of service(PLOS)is an important measure of performance in the analysis of existing pedestrian crosswalk conditions.Many researchers have developed PLOS models based on pedestrian delay,turning vehicle effect,etc.,using the conventional regression method.However,these factors may not effectively reflect the pedestrians'perception of safety while crossing the crosswalk.The conventional regression method has failed to estimate accurate PLOS because of the primary assumption of an arbitrary probability distribution and vagueness in the input data.Moreover,PLOS categories in existing studies are based on rigid threshold values and the boundaries that are not well defined.Therefore,it is an important attempt to develop a PLOS model with respect to pedestrian safety,convenience,and efficiency at signalized intersections.For this purpose,a video-graphic and user perception surveys were conducted at selected nine signalized intersections in Mumbai,India.The data such as pedestrian,traffic,and geometric characteristics were extracted,and significant variables were identified using Pearson correlation analysis.A consistent and statistically calibrated PLOS model was developed using fuzzy linear regression analysis.PLOS was categorized into six levels(A–F)based on the predicted user perception score,and threshold values for each level were estimated using the fuzzy c-means clustering technique.The developed PLOS model and threshold values were validated with the fieldobserved data.Statistical performance tests were conducted and the results provided more accurate and reliable solutions.In conclusion,this study provides a feasible alternative to measure pedestrian perception-based level of service at signalized intersections.The developed PLOS model and threshold values would be useful for planning and designing pedestrian facilities and also in evaluating and improving the existing conditions of pedestrian facilities at signalized intersections.