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CONTENTS
Volume 36, Number 6, December 2025
 

Abstract
In this study, a hydrocode analysis was performed to evaluate the behavior of pipe rack structures under blast loads. The LS-DYNA program was used for the simulation, and the reliability of the numerical model was verified using existing experimental data for column members subjected to blast loads. Typical pipe rack structures were then selected, and steel and reinforced concrete (RC) pipe rack structures were designed to yield equivalent performances based on structural design standards. Assuming a vapor cloud explosion scenario that occurs mainly in plant facilities, the blast load was calculated using the multi-energy method. The blast load was applied to the pipe rack structures, and structural responses such as column displacement and damage level were evaluated. A pressure-impulse (P-I) diagram was derived to identify the overpressure and impulse levels that induce specific damage states in each structure. The steel pipe rack structure showed a maximum support rotation of 2.47o, indicating high damage, while the RC structure exhibited only 0.22o, indicating minor damage. Fragility analysis results demonstrated that, under equivalent blast scenarios, the RC pipe rack structure had significantly lower failure probabilities than the steel structure. These results highlight the superior blast resistance of RC pipe racks and support risk-informed design through probabilistic evaluation.

Key Words
hydrocode; numerical model; P-I diagram; pipe rack

Address
Seung-Ho Choi: Department of Disaster Management and Fire Safety Engineering, University of Seoul, 163 Siripdaero, Dongdaemun-gu, Seoul, 02504, Korea
Kun-Ho Lee: Yunwoo Structural Engineers Co. Ltd 128, Beobwon-ro, Songpa-gu, Seoul, 05854, Korea
Jae-Hyun Kim: Department of Architectural Engineering, University of Seoul, 163 Siripdaero, Dongdaemun-gu, Seoul, 02504, Korea
Sang-Hoon Lee, Jaemin Kim and Kang Su Kim: Department of Architectural Engineering and Smart City Interdisciplinary Major Program, University of Seoul, 163 Siripdaero, Dongdaemun-gu, Seoul, 02504, Korea

Abstract
Here is analyzed the seismic performance of typical 15-story framed buildings of reinforced concrete in original state and retrofitted with different stiff solutions and combined with passive dampers by fast nonlinear time-history analyses. The investigated conventional retrofitting solutions are infill masonry walls, diagonal/Chevron braces, rigid cores and passive dampers (fluid viscous and solid viscoelastic). It is compared the floor shear/bending distribution, peak floor acceleration, seismic energy dissipation and device's hysteretic performance. The flexible behavior of the unretrofitted building was corrected with stiff retrofitting solutions, but, conversely, the peak floor accelerations were amplified (25-64%). Compared to conventional stiff solutions, the dampers allowed a reduction of distortions (floors 2-12), where the original building showed excessive flexibility. Viscoelastic dampers exhibited acceleration reductions on floors 4-12 (43%) and amplifications on top floors (15%). Conversely, fluid viscous dampers showed more acceleration reductions on almost all floors of about 55% and less amplifications on top floors. The presented results allowed to compare the floor acceleration flow with the use of stiff retrofitting solutions and the dissipation of the acceleration with the use of fluid viscous and solid viscoelastic dampers in strategic locations, as well as the energy dissipation capability of the devices. The energy absorption effectiveness of the dampers depended on the location, number of devices, shear forces and peak floor accelerations flow, especially when analyzed without dampers (only with stiff retrofitting). Fluid viscous dampers showed a better performance in terms of seismic energy absorption if compared to the combinations with viscoelastic dampers.

Key Words
earthquakes; energy absorption; fluid viscous dampers; passive dampers; peak floor acceleration; reinforce concrete framed buildings; solid viscoelastic dampers

Address
Department of Habitat and Urban Development, Western Institute of Technology and Higher Education (ITESO), 45604, Tlaquepaque, Jalisco, Mexico

Abstract
This study aims to explore the first-day compressive strength of ultra-high-performance concrete (UHPC), a crucial characteristic for its potentially diverse applications, by employing artificial neural networks (ANNs). In this research, a range of mineral additives to formulate UHPC, such as silica fume (SF), ground granulated blast furnace slag (GGBFS), metakaolin (MK), fly ash (FA), calcium carbonate (CaC), rice husk ash (RHA), fluid catalytic cracking catalyst residue (FC3R), quartz powder (QP), and glass waste powder (GWP) are investigated. Drawing from robust literature-based data containing 604 sets, we developed a precise ANN predictive model. The model's efficacy was appraised by comparing its regression outcomes with experimental findings from 90 UHPC samples, which served as benchmarks for validation. Here, we gauged the ANN regression's precision using various statistical metrics, notably the coefficient of determination (R2). The results underscore the efficacy of the ANN methodology in estimating UHPC's 1-day compressive strength, showing R2 of 0.912 and 0.898 for the test and validation groups, respectively, outperforming previous ANN models with a unique hidden layer. Furthermore, the Connection-Weight-Approach (CWA) technique was employed to explore the interaction between UHPC ingredients and early-age compressive strength. It was concluded that specific mineral additives, notably MK, SF, FC3R, and CaC, and features like particle packing density positively influenced UHPC's initial strength. However, the introduction of FA, GP, and RHA adversely affected its strength in the early stages.

Key Words
1-day compressive strength; ANN; CWA analysis; mineral admixtures; UHPC

Address
Joaquin Abellan-Garcia: Department of Civil and Environmental Engineering, Universidad Del Norte, Barranquilla, Colombia
M. Iqbal Khan and Yassir M. Abbas: Department of Civil Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
Andrea Castro-Cabeza: Helmut Schmidt University/University of the Federal Armed Forces Hamburg, Hamburg, Germany

Abstract
Concrete is widely utilized building material. With the integration of machine learning, particularly deep learning techniques, its performance assessment and mix optimization have become more data-driven and precise. By analyzing vast amounts of data, such as the properties of different materials, curing conditions, and strength test results, deep learning algorithms can identify patterns and make predictions. These models help eliminate guesswork by reducing dependency on trial-and-error methods, thereby lowering both time and cost. In this context, the current study explores the deep learning's application, specifically the Capsule Network (CapsNet), to predict the strength and concrete specimen's behavior such as cubes, cylinders, and beams. The main objective is to estimate the compressive strength of cubes, the tensile strength of cylinders, and the flexural strength of beams produced with various dosages of metakaolin, superplasticizers, and other conventional materials. For this purpose, a total of 75 cubes (150x150x150 mm), 15 beams (100x100x500 mm), and 15 cylinders (150x300 mm) were cast and tested under controlled laboratory conditions. The results demonstrated that the CapsNet model effectively captured the variations in mechanical performance, particularly enhancing the prediction of ultimate load-carrying capacity. This validates the machine learning-based approach's potential in improving concrete performance prediction and supporting intelligent material design.

Key Words
admixtures; capsule network; high strength concrete; metakaolin; superplasticizer

Address
Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Tamil Nadu, India

Abstract
Flat reinforced concrete (RC) slabs with openings near columns are susceptible to reduced punching shear capacity and serviceability issues. An experimental program was conducted on five two-way RC slabs (1600x1200x120 mm) under concentric column loading to evaluate the influence of opening shape, size, and location on punching shear behavior. The specimens included one solid slab and four slabs with openings: a square opening of 200x200 mm, a circular opening of 200 mm diameter, a square opening of 400x400 mm and a square opening of 200x200 mm offset 200 mm from the column face. Measurements encompassed crack patterns, load-deflection responses, and concrete strains. Results show that the solid slab developed typical radial punching cracks. The slab with a small square opening exhibited early corner cracking, whereas the slab with a circular opening had delayed crack initiation. The large square opening caused severe corner cracking and reduced the ultimate load by about 30%, whereas the small square opening led to only about a 10% reduction. In contrast, the circular opening had negligible effect on capacity. Notably, the circular opening enhanced the local tensile strain capacity beyond that of the solid slab (exceeding 100%), whereas the square opening slab retained only about 30%. Furthermore, the offset opening configuration improved stiffness recovery. These findings demonstrate that opening geometry strongly influences punching shear behavior: circular openings reduce stress concentrations and can even improve local tensile capacity, while positioning openings away from columns mitigates adverse effects, informing safer design practices for slab openings.

Key Words
circular opening; column loading; crack; slabs; solid slab; square opening

Address
T.Q.K. Lam: Faculty of Civil Engineering, Mien Tay Construction University, Vinh Long, Vietnam
T.K.O. Huynh: Construction Consultancy Center, Mien Tay Construction University, Vinh Long, Vietnam
H.K. Lam: Center for Continuing Education, Mien Tay Construction University, Vinh Long, Vietnam

Abstract
The enactment of the Turkish Building Earthquake Code (TBEC 2018) has prompted a number of significant developments in the analysis and design of structures. In particular, the procedures for determining the earthquake load differ significantly from those set forth in the TBEC-2007 regulation, which it replaced. One such distinction is the two-stage analysis methodology that has been introduced for the analysis of beamless slab systems. In this method, the column connections are modelled as monolithic in the initial stage of the calculation. Subsequently, in the second stage of the calculation, the column frames are modelled with bottom and top hinges, and the resulting structure is analysed. After the analysis, the internal forces in the columns, shear walls and slabs will be considered as the unfavourable one of the results obtained in the two stages, while the relative story drifts will be taken from the first stage, i.e., monolithic modeling. In this way, it is aimed to make a safe design even in the worst case scenario by taking into account the changes that may occur in the behavior of the structure and the cross-sectional effects of the load-bearing elements at the design stage. In this study, a building with a symmetrical flat slab system is modelled in accordance with the 2018 TBEC standard, and the behaviour of the structure is investigated by considering different shear wall ratios and thicknesses. For this purpose, a structure with 4 differently placed shear walls with thicknesses of 40 cm and 50 cm, was modeled using the Etabs program which analyses according to the finite element method. Shear wall lengths were calculated according to the determined shear wall ratios and analysed by modeling as pinned and monolithic as specified in the regulation. The period values, shear forces and displacements of the structure for 48 models obtained as a result are explained in detail with graphs and the behavior of the structure and the results of the 2-stage analysis method are evaluated.

Key Words
2-stage solution method; flat slab; shear wall ratios; TBEC 2018

Address
Department of civil Engineering, Necmettin Erbakan University, Konya, Türkiye

Abstract
This paper develops hybrid metaheuristic-based optimization methods for the estimation of the compression capacity of Concrete Filled Double Skin Steel Tubes (CFDSTs) columns under compression using Artificial Neural Networks (ANNs). With the proposed models, the weights, biases, and hidden layer size of the ANN are simultaneously optimized by using Artificial Bee Colony (ABC) optimization and Teaching Learning-Based Optimization (TLBO) algorithms, called ABC-ANN and TLBO-ANN models, respectively. A dataset containing 167 experiments reported in the literature is adopted to construct the models. It is found that both the ABC-ANN and TLBO-ANN methods are efficient in training predictive ANN models for the class of problems considered. The efficiency of the proposed models is demonstrated through the good comparisons with design standards and empirical formulae. A user-friendly Graphical User Interface (GUI) software based on the proposed models is built to conveniently estimate the axial compression capacity of CFDST columns. By performing reliability analyses using Monte Carlo simulations, the strength reduction factors are suggested to ensure the GUI program applicable for practical design applications. Finally, an optimization procedure is developed based on the proposed ABC-ANN model to determine the optimal design of CFDST columns.

Key Words
artificial bee colony; artificial neural networks; concrete filled double skin steel tubes; reliability analysis; teaching learning-based optimization

Address
Quang-Viet Vu: 1) Faculty of Advanced Technology and Engineering, VNU Vietnam Japan University, Hanoi, Vietnam, 2) Center of Excellence in Applied Mechanics and Structures, Department of Civil Engineering, Chulalongkorn University, Bangkok 10330, Thailand
Nhu Son Doan: Faculty of Civil Engineering, Vietnam Maritime University, 484 Lach Tray, Hai Phong, Vietnam
George Papazafeiropoulos: School of Civil Engineering, National Technical University of Athens, Zografou, Athens 15780, Greece
Wei Gao: Centre for Infrastructure Engineering and Safety, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia
Sawekchai Tangaramvong: Center of Excellence in Applied Mechanics and Structures, Department of Civil Engineering, Chulalongkorn University, Bangkok 10330, Thailand

Abstract
The investigation of electrically conductive concrete as a new generation building material has gathered great attention from researchers. The broadest aspect of the produc-tion and research purposes of conductive concrete is heatable slabs. However, experi-mental electrothermal tests of this type of building materials are costly in terms of la-bor and production and involve time-consuming processes. In this study, electrother-mal analyzes were carried out using the Abaqus program to investigate the effect of the cross-sectional geometry between the electrodes on the thermal performance. The aim was to estimate a suitable cross-section geometry with low cost and time. In the analysis, applicable sections with 6 different geometries and a width of one meter were preferred. Concrete properties were obtained from the author's previous experimental studies. A potential difference of 24 V was defined in all models throughout the simu-lation period. According to the electrothermal analysis results, the increase in total volume caused the resulting temperature and heat energy to increase. The analysis demonstrated that optimizing the cross-sectional area of the models led to improved temperature distribution and heat energy efficiency while achieving significant concrete volume savings, highlighting the potential for more sustainable design approaches.

Key Words
conductive concrete; electrical power; electrothermal analysis; FE method; heat energy

Address
Heydar Dehghanpour: Department of Civil Engineering, Faculty of Engineering, Istanbul Aydin University, Kucukcekmece, Istanbul, Türkiye
Muhammed Marasli and Volkan Ozdal: Department of R&D, Fibrobeton Inc., Duzce,Türkiye
Serkan Subasi: Department of Civil Engineering, Faculty of Engineering, Duzce University, Duzce, Türkiye

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