Majiagou landslide, a major ancient landslide in Three Gorges Reservoir region, is located in the high earthquake area of southwest China. The 2013 Badong earthquake caused an obvious deformation of landslide monitored by the sliding inclinometer. A strong earthquake may induce the reactivation of ancient landslide. So, it is necessary to research the seismic dynamic response of Majiagou landslide. For this purpose, discontinuous deformation analysis (DDA), improved by introducing the artificial joint and viscous boundary, is applied in this study. The displacements at monitoring points caused by Badong earthquake are calculated and compared with the field data, verifying the numerical method and model. Further, a strong earthquake with the peak acceleration of 1&#x2009;g is assumed to act on the landside, the initiation and evolution process of landslide is simulated, and the movement features of landslide are discussed. The dynamic failure of landslide and the local amplification of seismic wave can be embodied, indicating that the improved DDA provides an alternative approach for analyzing the seismic dynamic response of jointed rock.

Xiu-Li Zhang

Bi-Ting Xie

Hao Wang

Yu-Yong Jiao

Study on the Dynamic Response of Landslide Subjected to Earthquake by the Improved DDA Method

Nowadays, inventory management is a tool that must be extended to cover all aspects of the supply chain (SC). One of these aspects is Sustainable Development (SD) which emphasizes the balance between economic well-being, natural resources, and society. As inventory involves the use of natural and economic resources, the integration of SD criteria is important for a more efficient and sustainable SC. In this work, the most important SD variables associated with inventory management were identified. These variables were integrated as cost elements within a nondeterministic inventory control model to include SD criteria within inventory supply strategies. Through the assessment of the proposed integrated model, it was determined that, although SD practices involve additional investments, specific practices such as reuse/recycling and government incentives can increase revenue and profits. This is important for the development of government and business strategies to perform sustainable practices.

Gladys Bonilla-Enriquez

Patricia Cano-Olivos

Li-Qun Peng

Weihua Gan

Jose-Luis Martinez-Flores

Diana Sanchez-Partida

Modelling Sustainable Development Aspects within Inventory Supply Strategies

In this work, we propose a high-order algorithm based on the asymptotic numerical method (ANM) for the nonlinear elastic computation of helical structures without neglecting any nonlinear term. The nonlinearity considered in the following study will be a geometric type, and the kinematics adopted in this numerical modeling takes into account the hypotheses of Timoshenko and de Saint-Venant. The finite element used in the discretization of the middle line of this structure is curvilinear with twelve degrees of freedom. Using a simple example, we show the efficiency of the algorithm which was carried out in this context and which resides in the reduction of the number of inversions of the tangent matrix compared to the incremental iterative algorithm of Newton-Raphson.

Fatima Boussaoui

Hassane Lahmam

Bouazza Braikat

Numerical High-Order Model for the Nonlinear Elastic Computation of Helical Structures

Energy consumption in buildings especially in offices is alarming and prompts the desire for more energy analysis work to be done in testing models that can estimate the energy situation of commercial buildings, and the key contributing factors are based on human factors, work load, and weather variables like solar radiation and temperature. In the research, the administration block of the University of Energy and Natural Resources, Ghana, was selected and modeled for energy analysis using SketchUp. Daily energy consumption of the building was generated with EnergyPlus indicating the electricity consumption of the block for the year 2018 for which 68.7% was used by equipment in the block, 26.98% on cooling, and the rest on lighting. The Artificial Neural Network model which had weather variable and days as input neurons and cooling, lighting, equipment, and total building electricity consumption as output neurons was modeled in MATLAB. The model after training had <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2064009pt" id="M1" height="8.8423pt" version="1.1" viewBox="-0.0498162 -8.6359 8.28119 8.8423" width="8.28119pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-83" d="M610 18C585 26 567 34 540 68C517 97 499 128 476 171C452 215 425 276 413 304C496 332 570 394 570 494C570 555 545 595 509 619S419 650 364 650H139L133 622C216 615 219 612 203 527L129 132C112 40 105 36 23 28L17 0H279L285 28C199 34 194 40 211 132L239 284H284C320 284 334 275 351 236C374 182 394 140 420 93C459 23 495 -1 592 -8H600L610 18ZM480 485C480 424 449 372 403 342C374 323 338 316 293 316H245L291 562C296 589 301 601 311 608S337 618 358 618C432 618 480 575 480 485Z"/></g></svg> values for training, validation, and testing to be 0.999 and validation performance of <span class="inline_break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="25.983pt" style="vertical-align:-0.2063999pt" id="M2" height="11.7978pt" version="1.1" viewBox="-0.0498162 -11.5914 25.983 11.7978"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-50" d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"/></g><g transform="matrix(.013,0,0,-0.013,6.24,0)"><path id="g113-47" d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 -12Z"/></g><g transform="matrix(.013,0,0,-0.013,9.204,0)"><path id="g113-56" d="M447 623H65C61 580 56 530 47 475H76C100 541 106 550 172 550H388C308 376 196 170 91 -1L98 -12L172 -2C268 204 360 408 455 611L447 623Z"/></g><g transform="matrix(.013,0,0,-0.013,18.352,0)"><path id="g113-43" d="M471 153C471 170 463 194 452 212C400 220 373 229 322 255C373 281 400 290 452 298C463 316 471 339 471 357C456 366 431 371 410 370C377 329 356 310 308 279C311 336 317 364 336 413C326 432 310 451 294 459C279 451 262 432 252 413C271 364 277 336 280 279C232 310 211 329 178 370C157 371 132 367 117 357C117 340 125 316 136 298C188 290 215 281 266 255C215 229 188 220 136 212C125 194 117 171 117 153C132 144 157 139 178 140C211 181 232 200 280 231C277 174 271 146 252 97C262 78 278 59 294 51C309 59 326 78 336 97C317 146 311 174 308 231C356 200 377 181 410 140C431 139 456 143 471 153Z"/></g></svg><span class="ibiop"></span><span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="27.687pt" style="vertical-align:-0.2063999pt" height="11.7978pt" version="1.1" viewBox="28.838183800000003 -11.5914 27.687 11.7978"><g transform="matrix(.013,0,0,-0.013,28.888,0)"><use xlink:href="#g113-50"/></g><g transform="matrix(.013,0,0,-0.013,35.128,0)"><path id="g113-49" d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"/></g><g transform="matrix(.0091,0,0,-0.0091,41.415,-5.741)"><path id="g54-33" d="M556 236V289H56V236H556Z"/></g><g transform="matrix(.0091,0,0,-0.0091,46.975,-5.741)"><path id="g50-49" d="M245 635C92 635 37 457 37 312C37 149 91 -12 244 -12C395 -12 449 166 449 312C449 469 395 635 245 635ZM243 598C332 598 358 454 358 312C358 173 334 26 245 26C158 26 128 174 128 313S152 598 243 598Z"/></g><g transform="matrix(.0091,0,0,-0.0091,51.407,-5.741)"><path id="g50-53" d="M462 177V227H365V632H320C217 496 116 350 21 208V177H284V109C284 43 280 38 190 31V0H451V31C369 38 365 43 365 108V177H462ZM284 227H88C155 336 218 430 282 519H284V227Z"/></g></svg>.</span></span> It was able to predict the energy consumption for lighting, cooling, and equipment very close to the results with minimal. The results from the ANN model prediction were compared with the EnergyPlus simulations. The maximum deviation profile for the following parameters (lighting, cooling, and equipment) is 13%, 8%, and 4%, respectively. The large difference in the lighting and cooling is the difficulty involved in predicting human behaviour and weather conditions. The least value recorded for the equipment is due to its independence on external factors.

Felix Uba

Holali Kwami Apevienyeku

Freda Dapaa Nsiah

Alex Akorli

Stephen Adjignon

Energy Analysis of Commercial Buildings Using Artificial Neural Network

A finite-element (FE) numerical study using PLAXIS-3D software was carried out to reproduce and validate a full-scale experimental <i>in situ</i> test and to investigate the earth pressure on a flexible temporary trench box shield in soft and sensitive clay soil. The excavation trench model was 6 m (20 ft) deep and was considered as nonlinear and anisotropic clay. A 45 kPa (0.94 ksf) surface overload on top of the soil near the trench box was also simulated to produce a maximum load case on the flexible wall of the shield. Both Mohr-Coulomb (MC) and hardening soil (HS) constitutive soil models were considered for FE analysis. Different values of the modulus reduction factor (MRF) and the coefficient of earth pressure at rest (<span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.291101pt" id="M1" height="11.927pt" version="1.1" viewBox="-0.0498162 -8.6359 14.3107 11.927" width="14.3107pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-76" d="M743 650H503L496 622L527 618C563 613 564 603 532 573C449 495 371 431 323 392C301 374 272 355 246 346L280 522C297 609 300 614 379 622L385 650H135L129 622C209 614 215 609 198 522L124 133C106 39 99 35 23 28L17 0H271L277 28C193 35 192 39 208 133L239 316C264 328 280 325 303 288C368 183 435 90 502 0H652L659 28C602 34 584 43 543 94C495 154 403 283 347 369L574 554C634 603 659 612 735 624L743 650Z"/></g><g transform="matrix(.0091,0,0,-0.0091,9.23,3.132)"><path id="g50-49" d="M245 635C92 635 37 457 37 312C37 149 91 -12 244 -12C395 -12 449 166 449 312C449 469 395 635 245 635ZM243 598C332 598 358 454 358 312C358 173 334 26 245 26C158 26 128 174 128 313S152 598 243 598Z"/></g></svg>)</span> were considered to validate the model. For a specific shear strength profile, FE analysis with a linear elastoplastic soil model showed relatively small differences in soil pressure with the field test results along the depth of the trench. Results were also compared with the predictions of well-established analytical formulae.

Miah Alam

Omar Chaallal

Bertrand Galy

Flexible Temporary Shield in Soft and Sensitive Clay: 3D FE Modelling of Experimental Field Test

This paper presents the qualitative precipitation estimation (QPE) based on data from the Himawari satellite and distributed-domain specific architecture. The QPE process consists of receiving and managing the raw data from the satellite every 10 minutes and calculating the rain-temperature relationship. The aim of this research is to decrease the QPE processing time by using distributed domain-specific architecture (DDSA), with 9 small computing boards are connected to a gigabit switch. Instead of using a high-performance PC, this distributed embedded system is also suitable for processing interval data receiving from the satellite every 10 minutes. The experimental results show that the proposed fast-satellite data processing algorithm is optimal for QPE processing on the DDSA platform, requiring 115.53 seconds processing time and low power consumption.

Sethakarn Prongnuch

Theerayod Wiangtong

Suchada Sitjongsataporn

Qualitative Precipitation Estimation from Satellite Data Based on Distributed Domain-Specific Architecture

Ehsan Sarhadizadeh

Kourosh Hejazi

Corrigendum to &#x201C;Eulerian Oil Spills Model Using Finite-Volume Method with Moving Boundary and Wet-Dry Fronts&#x201D;

In this paper, we investigate the numerical approximation solution of parabolic and hyperbolic equations with variable coefficients and different boundary conditions using the space-time localized collocation method based on the radial basis function. The method is based on transforming the original <span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2063999pt" id="M1" height="9.49473pt" version="1.1" viewBox="-0.0498162 -9.28833 7.34169 9.49473" width="7.34169pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-101" d="M530 686C535 705 530 712 521 712C504 712 448 684 359 674L358 648H393C437 648 439 646 429 593L400 435C372 447 345 448 332 448C286 448 194 414 144 373C68 311 23 203 23 111C23 26 57 -12 91 -12C120 -12 147 3 188 29C227 54 290 102 341 170H343L322 71C308 6 320 -12 341 -12C373 -12 442 27 501 96L485 120C455 91 422 67 408 67C401 67 401 76 404 91C440 294 479 473 530 686ZM387 375L355 241C326 187 200 53 142 53C126 53 109 73 109 130C109 217 154 337 218 381C240 396 265 404 297 404S372 390 387 375Z"/></g></svg>-</span>dimensional problem in space into <span class="inline_break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="22.25pt" style="vertical-align:-2.26807pt" id="M2" height="11.5564pt" version="1.1" viewBox="-0.0498162 -9.28833 22.25 11.5564"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-41" d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z"/></g><g transform="matrix(.013,0,0,-0.013,4.498,0)"><use xlink:href="#g113-101"/></g><g transform="matrix(.013,0,0,-0.013,14.619,0)"><path id="g117-36" d="M535 230V280H323V490H265V280H52V230H265V-3H323V230H535Z"/></g></svg><span class="ibiop"></span><span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="10.95pt" style="vertical-align:-2.26807pt" height="11.5564pt" version="1.1" viewBox="25.105183800000002 -9.28833 10.95 11.5564"><g transform="matrix(.013,0,0,-0.013,25.155,0)"><path id="g113-50" d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"/></g><g transform="matrix(.013,0,0,-0.013,31.395,0)"><path id="g113-42" d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z"/></g></svg>-</span></span>dimensional one in the space-time domain by combining the <span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2063999pt" id="M3" height="9.49473pt" version="1.1" viewBox="-0.0498162 -9.28833 7.34169 9.49473" width="7.34169pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-101"/></g></svg>-</span>dimensional vector space variable and <span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.04981995pt" id="M4" height="8.39864pt" version="1.1" viewBox="-0.0498162 -8.34882 6.36303 8.39864" width="6.36303pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-50"/></g></svg>-</span>dimensional time variable in one <span class="inline_break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="22.25pt" style="vertical-align:-2.26807pt" id="M5" height="11.5564pt" version="1.1" viewBox="-0.0498162 -9.28833 22.25 11.5564"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-41"/></g><g transform="matrix(.013,0,0,-0.013,4.498,0)"><use xlink:href="#g113-101"/></g><g transform="matrix(.013,0,0,-0.013,14.619,0)"><use xlink:href="#g117-36"/></g></svg><span class="ibiop"></span><span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="10.95pt" style="vertical-align:-2.26807pt" height="11.5564pt" version="1.1" viewBox="25.105183800000002 -9.28833 10.95 11.5564"><g transform="matrix(.013,0,0,-0.013,25.155,0)"><use xlink:href="#g113-50"/></g><g transform="matrix(.013,0,0,-0.013,31.395,0)"><use xlink:href="#g113-42"/></g></svg>-</span></span>dimensional variable vector. The advantages of such formulation are (i) time discretization as implicit, explicit, <span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2063999pt" id="M6" height="9.49473pt" version="1.1" viewBox="-0.0498162 -9.28833 6.59789 9.49473" width="6.59789pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-230" d="M475 507C475 612 440 712 326 712C139 712 23 420 23 215C23 96 58 -12 180 -12C369 -12 475 293 475 507ZM391 522C391 486 387 448 379 394H126C155 538 222 677 310 677C386 677 391 571 391 522ZM373 346C344 193 283 22 189 22C126 22 106 114 106 196C106 243 111 293 118 346H373Z"/></g></svg>-</span>method, method-of-line approach, and others are not applied; (ii) the time stability analysis is not discussed; and (iii) recomputation of the resulting matrix at each time level as done for other methods for solving partial differential equations (PDEs) with variable coefficients is avoided and the matrix is computed once. Two different formulations of the <span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2063999pt" id="M7" height="9.49473pt" version="1.1" viewBox="-0.0498162 -9.28833 7.34169 9.49473" width="7.34169pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-101"/></g></svg>-</span>dimensional problem as a <span class="inline_break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="22.25pt" style="vertical-align:-2.26807pt" id="M8" height="11.5564pt" version="1.1" viewBox="-0.0498162 -9.28833 22.25 11.5564"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-41"/></g><g transform="matrix(.013,0,0,-0.013,4.498,0)"><use xlink:href="#g113-101"/></g><g transform="matrix(.013,0,0,-0.013,14.619,0)"><use xlink:href="#g117-36"/></g></svg><span class="ibiop"></span><span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="10.95pt" style="vertical-align:-2.26807pt" height="11.5564pt" version="1.1" viewBox="25.105183800000002 -9.28833 10.95 11.5564"><g transform="matrix(.013,0,0,-0.013,25.155,0)"><use xlink:href="#g113-50"/></g><g transform="matrix(.013,0,0,-0.013,31.395,0)"><use xlink:href="#g113-42"/></g></svg>-</span></span>dimensional space-time one are discussed based on the type of PDEs considered. The localized radial basis function meshless method is applied to seek for the numerical solution. Different examples in two and three-dimensional space are solved to show the accuracy of such method. Different types of boundary conditions, Neumann and Dirichlet, are also considered for parabolic and hyperbolic equations to show the sensibility of the method in respect to boundary conditions. A comparison to the fourth-order Runge-Kutta method is also investigated.

Mohammed Hamaidi

Ahmed Naji

Ahmed Taik

Solving Parabolic and Hyperbolic Equations with Variable Coefficients Using Space-Time Localized Radial Basis Function Collocation Method

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<p><em>Modelling and Simulation in Engineering</em> aims to provide a forum for the discussion of formalisms, methodologies and simulation tools which relate to the modelling and simulation of human-centred engineering systems.</p>


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Modelling and Simulation in Engineering | Hindawi

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Modelling and Simulation in Engineering aims to provide a forum for the discussion of formalisms, methodologies and simulation tools which relate to the modelling and simulation of human-centred engineering systems.

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Modelling and Simulation in Engineering