Advances in Engineering Software最新文献

{"title":"Effects of inclined loads on strip footings with an underlying tunnel","authors":"Gaoqiao Wu ,&nbsp;Jiayu Zeng ,&nbsp;Rui Zhang ,&nbsp;Yongjie Tan ,&nbsp;Shiping Zhang","doi":"10.1016/j.advengsoft.2024.103783","DOIUrl":"10.1016/j.advengsoft.2024.103783","url":null,"abstract":"<div><div>The self-developed Finite Element Limit Analysis (FELA) code was utilized to examine the ultimate bearing capacity of strip footings positioned above tunnels affected by inclined loads. Bearing capacity factors were predicted using both upper bound (UB) and lower bound (LB) solutions, with a variance of less than 3 %. The primary focus of this study lies in assessing the influences of underlying tunnels and inclined loads on potential failure modes. In particular, the concept of failure envelopes was introduced, by which the load properties (inclination angle), the tunnel location (relative lateral distance and longitudinal distance from the footing) and the material properties of rock masses (<em>GSI, m</em>_i, <em>s</em>_ci, <em>g</em>) were involved in to facilitate preliminary designs. In addition to envelopes, the transition among failure patterns was summarized, resting with any possible factors.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"198 ","pages":"Article 103783"},"PeriodicalIF":4.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142426487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated construction safety reporting system integrating deep learning-based real-time advanced detection and visual question answering","authors":"Shihao Wen ,&nbsp;Minsoo Park ,&nbsp;Dai Quoc Tran ,&nbsp;Seungsoo Lee ,&nbsp;Seunghee Park","doi":"10.1016/j.advengsoft.2024.103779","DOIUrl":"10.1016/j.advengsoft.2024.103779","url":null,"abstract":"<div><div>The construction sector is globally acknowledged as one of the most hazardous industries, owing to the vulnerability of its workers to accidents, injuries, and even loss of life. Effective precautionary measures are necessary and ensuring the use of personal protective equipment (PPE) by workers is crucial for protecting them from accidents. Existing deep learning-based PPE detection systems mainly use simple vision-based target detection methods for tasks such as the identification of helmets or vests, and they tend to be task-specific. However, the identification of specific PPE based on respective job types and maintaining detailed safety records, requires further innovative approaches. In this paper, we propose an innovative intelligent system that not only accurately recognizes specific PPE according to the needs of different work types but also automatically generates safety inspection reports and establishes complete safety records, thus providing critical data to support accident investigations. The proposed system integrates a target detection model, visual question answering model, and text-based analysis of the relevant regulations to realize real-time detection of PPE and automatic generation of safety inspection reports. The experimental results show that the proposed YOLOv8n-DCA network strikes a good balance between performance and computational cost—, with a mAP value of 86%. Compared to the original YOLOv8n network, the mAP value is improved by 5.1%, while the model parameters and size are significantly reduced. Further, the visual question answering model exhibited a precision is 95.9. Finally, the automatic generation of safety inspection reports was successfully realized, verifying the feasibility of the developed system. This innovative system promises a comprehensive and efficient PPE management solution for the construction industry to ensure worker safety and provide strong data support.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"198 ","pages":"Article 103779"},"PeriodicalIF":4.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142426496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-threaded parallel tetrahedral mesh improvement by combining atomic operation and graph coloring","authors":"Yifu Wang,&nbsp;Junji Wang,&nbsp;BoHan Wang,&nbsp;Yifei Wang,&nbsp;Jianjun Chen","doi":"10.1016/j.advengsoft.2024.103782","DOIUrl":"10.1016/j.advengsoft.2024.103782","url":null,"abstract":"<div><div>In industrial numerical simulations, efficiently generating high-quality tetrahedral meshes remains a significant challenge. Advances in high-performance computing have made parallelization a practical approach to improving the quality of large-scale tetrahedral meshes. This study proposes a fine-grained multithreaded parallel method to accelerate tetrahedral mesh improvement. By utilizing atomic operations, we fundamentally address thread safety concerns. Additionally, through the precise use of atomic operations, task decomposition strategies, and a multithreaded memory model, we minimize the probability of task overlap and data races, thereby enhancing overall parallel mesh improvement efficiency. Experimental results demonstrate that our parallel mesh improver is robust and effective for complex industrial models. On a laptop with 16 threads, we achieved a tenfold increase in tetrahedral mesh improvement speed, with the quality of the improved meshes being comparable to that of the sequential process.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"198 ","pages":"Article 103782"},"PeriodicalIF":4.0,"publicationDate":"2024-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-regional MFSE topology optimization method for large-scale structures with arbitrary design domains","authors":"Zhaoyou Sun ,&nbsp;Tingxi Yuan ,&nbsp;Wenbo Liu ,&nbsp;Jiaqi He ,&nbsp;Tiejun Sui ,&nbsp;Yangjun Luo","doi":"10.1016/j.advengsoft.2024.103778","DOIUrl":"10.1016/j.advengsoft.2024.103778","url":null,"abstract":"<div><div>Due to its exceptional mechanical properties, large-scale topology optimization with arbitrary design domains has become an attractive mission and facilitated the application of topology optimization methods in practical engineering applications. In this work, an extended material-field series expansion (MFSE) method that employs a multi-regional strategy with spatial-varied correlation length is proposed for arbitrary design domain and overcoming several shortcomings of the original MFSE method. The proposed approach involves dividing the design domain into multiple sub-regions through background grid mapping technology, where each sub-region is characterized by its own material field function. The evolution of these material-field functions is carried out independently driven by the design sensitivity of the objective function and constraints. As expected, the structures in any two adjacent sub-regions can be connected perfectly due to the continuity of the solution by mono-scale analysis. The proposed framework is scalable and can be utilized for parallel computation, arbitrary design domains, and different topology optimization problems. Several numerical examples, including 2D and 3D design domains with arbitrary geometries, are presented to validate the effectiveness of the proposed method in applying large-scale structures with arbitrary design domains.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"198 ","pages":"Article 103778"},"PeriodicalIF":4.0,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic static analysis of functionally graded sandwich nanoplates based on a novel stochastic meshfree computational framework","authors":"Baikuang Chen ,&nbsp;Zhanjun Shao ,&nbsp;A.S. Ademiloye ,&nbsp;Delei Yang ,&nbsp;Xuebing Zhang ,&nbsp;Ping Xiang","doi":"10.1016/j.advengsoft.2024.103780","DOIUrl":"10.1016/j.advengsoft.2024.103780","url":null,"abstract":"<div><div>In this study, the spatial variability of materials is incorporated into the static analysis of functionally graded sandwich nanoplates to achieve higher accuracy. Utilising a modified point estimation method and the radial point interpolation method, we develop a novel stochastic meshfree computational framework to deal with the material uncertainty. Higher-order shear deformation theory is employed to establish the displacement field of the plates. The elastic modulus of ceramic and metal (<em>E</em>_c and <em>E</em>_m) are treated as separate random fields and discretized through the Karhunen-Loève expansion (KLE) method. To improve the performance of procedure, the Wavelet-Galerkin method is introduced to solve the second type of Fredholm integral equation. Subsequently, substituting the random variables obtained by KLE into the stochastic computational framework, a high accuracy stochastic response of structures can be acquired. By comparing computed findings with those of Monte Carlo simulation, the accuracy and efficiency of developed framework are verified. Moreover, the results indicate that the plate's deflection exhibits varying sensitivities to the random fields <em>E</em>_c and <em>E</em>_m. Also, the sandwich configuration as well as power-law exponents affect the stochastic response of structures. These findings offer valuable insights for the optimized design of functionally graded sandwich nanoplates.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"198 ","pages":"Article 103780"},"PeriodicalIF":4.0,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simultaneous optimization of capacity and topology of seismic isolation systems in multi-story buildings using a fuzzy reinforced differential evolution method","authors":"Ali Mortazavi,&nbsp;Elif Çağda Kandemir","doi":"10.1016/j.advengsoft.2024.103781","DOIUrl":"10.1016/j.advengsoft.2024.103781","url":null,"abstract":"<div><div>Inter-story isolation systems, as an alternative earthquake protection system, reduce in-building movement compared to base isolation systems. In this context, the current study focuses on simultaneously optimizing the topology and capacity of base and inter-story isolation systems for a multi-story building exposed to multiple earthquake scenarios. In addressing this challenge, an optimization model is developed that simultaneously considers both the topology (vertical arrangement) and capacity (required stiffness) of the seismic isolators as the decision variables of the model. To attain more practical and feasible solutions, the side constraints of the problem involve the inter-story drift and the total cost of seismic isolation systems. A gradient-free and self-adaptive search method, Fuzzy Differential Evolution incorporated Virtual Mutant (FDEVM), is employed to solve the optimization problem. The FDEVM approach applies a fuzzy mechanism to adopt its search behavior with governing condition(s) of the current problem. The selected method's performance is implicitly compared with its standard version. The obtained results indicate that optimally placing inter-story isolators with an optimal configuration and capacity not only improves the seismic performance of the systems but also its more cost-efficient approach compared with conventional based isolation systems. Also, the comparative outcomes indicate that the FDEVM method exhibits a high search capability for this class of problems.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"198 ","pages":"Article 103781"},"PeriodicalIF":4.0,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerated segregated finite volume solvers for linear elastostatics using machine learning","authors":"Scott Levie,&nbsp;Philip Cardiff","doi":"10.1016/j.advengsoft.2024.103763","DOIUrl":"10.1016/j.advengsoft.2024.103763","url":null,"abstract":"<div><div>The segregated solution algorithm is widely used for solving finite volume continuum mechanics problems. One major contributor to the computational time requirement of this approach is the high number of outer iterations needed to achieve convergence. The methodology proposed in this work aims to decrease the computational time required by employing an artificial neural network to predict converged solution fields for linear elastostatic finite volume analyses. The machine learning model is trained on coarse mesh data using a sequence of consecutive initial unconverged displacement fields as inputs and the converged displacement field as the target. Subsequently, the trained model is used to predict the converged displacement field for a fine mesh case. The speedup calculation incorporates the time required to run the coarse mesh case and train the machine learning model. The typical speedups achieved using the proposed technique in this study range between 2 and 4. However, it has the potential to achieve higher speedups, with the maximum observed in this study being 13.3.</div></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"198 ","pages":"Article 103763"},"PeriodicalIF":4.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0965997824001704/pdfft?md5=2f248d5cd01074e0e68b9bc10612f237&pid=1-s2.0-S0965997824001704-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Topology optimization of periodic structures under multiple dynamic uncertain loads","authors":"Jinhu Cai,&nbsp;Jing Huang,&nbsp;Long Huang,&nbsp;Qiqi Li,&nbsp;Lairong Yin","doi":"10.1016/j.advengsoft.2024.103777","DOIUrl":"10.1016/j.advengsoft.2024.103777","url":null,"abstract":"<div><p>Periodic structures have attracted considerable attention in lightweight design due to their high specific strength and stiffness. Despite this, existing topology optimization research on these structures typically focuses on deterministic, single-load cases. To address the limitations arising from real-world, variable load conditions, this study presents a robust method for the topology optimization of periodic structures under both multiple and uncertain load cases. The proposed model integrates the uncertainty of the load magnitude, direction, and excitation frequency, employing the weighted sum of the mean and standard deviation of the dynamic structural compliance modulus as the objective function, constrained by the volume fraction of the structure. A method for uncertainty quantification is introduced, utilizing the bivariate dimension reduction technique and Gauss-type quadrature. Leveraging the displacement superposition principle in linear elastomers, we provide a method to calculate the mean and standard deviation of the dynamic structural compliance modulus under these complex load cases. Additionally, the sensitivity of the objective function concerning design variables is derived. The effectiveness of the proposed method is verified through numerical examples, revealing the effect of load uncertainty on the topology optimization of periodic structures.</p></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"198 ","pages":"Article 103777"},"PeriodicalIF":4.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive resilience assessment of bridge networks using ensemble learning method","authors":"Guojun Yang ,&nbsp;Dongxu Wu ,&nbsp;Jianbo Mao ,&nbsp;Yongfeng Du","doi":"10.1016/j.advengsoft.2024.103774","DOIUrl":"10.1016/j.advengsoft.2024.103774","url":null,"abstract":"<div><p>The assessment of seismic resilience in bridge networks holds significant importance for urban disaster prevention and mitigation efforts. Unlike individual bridges, there has been limited efficiency in assessing bridge networks. A seismic resilience assessment methodology for bridge networks using ensemble learning methods is proposed in this paper. Initially, a comprehensive resilience index is proposed, integrating both structural and functional aspects of bridge networks. Using 3 ensemble learning methods, 9 parameters related to network structure and traffic characteristics are chosen as input variables for predicting the seismic resilience index. Finite element models of 18 bridges are constructed and combined to generate 3500 sets of virtual bridge networks for model training. The predictive accuracy of models trained using the 3 ensemble methods exceeds 89 %, and the expected values of peak ground acceleration (<em>PGA</em>) and functional loss rate are the most influential features. The methodology offers insights into the application of ensemble learning for bridge network seismic resilience assessment.</p></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"198 ","pages":"Article 103774"},"PeriodicalIF":4.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142172735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image reconstruction based on nonconvex overlapping group sparse regularization for planar ECT defect detection","authors":"Zhihao Tang,&nbsp;Lifeng Zhang","doi":"10.1016/j.advengsoft.2024.103767","DOIUrl":"10.1016/j.advengsoft.2024.103767","url":null,"abstract":"<div><p>Composite materials have been widely applied in aerospace, automotive, and construction industries, making the non-destructive testing of these materials crucial. Planar electrical capacitance tomography (ECT), as a permittivity visualization technology, holds significant potential for development in the field of non-destructive testing. However, the underdetermination of its inverse problem often poses a key challenge to the imaging quality. To alleviate the underdetermination of the inverse problem and improve the image reconstruction quality of planar ECT, an image reconstruction method based on nonconvex overlapping group sparsity (NOGS) regularization is proposed. Firstly, the <em>l</em>_2,1 overlapping group sparse regularization model for normalized permittivity is established. Secondly, nonconvex functions are utilized as the external functions of the <em>l</em>_2,1 norm to form a NOGS regularization model. Finally, a Fast Non-Convex Overlapping Group Sparse Algorithm (FaNogSa) based on the LBP solution is proposed to solve the model for image reconstruction. To validate the effectiveness of this method, simulations, and experiments are conducted, and comparisons are made with the Tikhonov algorithm, Landweber algorithm, <em>l</em>₁ norm method, Laplace Prior-Based Efficient Sparse Bayesian Learning (L-ESBL), student's T Prior-Based Efficient Sparse Bayesian Learning (S-ESBL), and method by combining the density-based spatial clustering of applications with noise clustering algorithm and self-adaptive alternating direction method of multipliers (DBSCAN-SADMM) algorithm. Results demonstrate that NOGS outperforms other algorithms in terms of reconstruction accuracy, convergence time, and robustness. Among NOGS, NOGS (atan) performs the best, NOGS (abs) performs the worst, and NOGS (log) falls in between.</p></div>","PeriodicalId":50866,"journal":{"name":"Advances in Engineering Software","volume":"198 ","pages":"Article 103767"},"PeriodicalIF":4.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142161674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}