Interconnect Parameters Research Articles

Use of a multi-criteria approach to analyze the degradation of reinforced concrete beam bridges. Three case studies in the north west of Algeria

The main motivation for this research is to develop assessment and estimation tools for the degradation state of existing civil engineering structures. The longevity of a structure is influenced by a complex set of interconnected phenomena and parameters. Therefore, we adopt a multicriteria approach aimed at better identifying these elements, particularly the most influential ones. This study focuses on assessing the degradation of reinforced concrete beam bridges by developing a digital tool based on the Analytic Hierarchy Process (AHP). We have identified 30 factors impacting degradation, categorized as internal factors, such as the condition of structural elements, and external factors, such as age and environment. From these factors, we calculate a Global Bridge Degradation Index (GBDI) that reflects the overall condition of the bridge. This index allows us to establish a classification of the bridges, ranging from no danger to a critical state requiring immediate intervention. The innovation of our model lies in its ability to provide a quantified estimation of the degradation state while incorporating an unlimited number of factors and criteria. After applying this model to three bridges in Algeria, we observed satisfactory results. The ultimate goal is to provide managers with tools for reflection and comparison to assess the condition of bridges and consider the necessary actions for their maintenance, repair, or replacement, thereby contributing to the improvement of the management and safety of road infrastructures.

Probing the complexities of optimal flow in open channels: experimental and numerical validation with diversion flow

ABSTRACT This study aims to establish the optimal diversion angle for bifurcating channels to minimize separation zone size in the intake channel while maximizing discharge in the bifurcating channel through experimental and numerical investigations. The study successfully accomplished its goals by employing the Flow-3D 11.0.4 software. The software was utilized to examine the flow diversion into bifurcating channels with various diversion angles, including 900°, 750°, 600°, 450°, 300°, 250°, 200° and 150°. The experimental investigation has confirmed the theoretical predictions regarding the expected flow characteristics. The conclusive findings demonstrated that the diverted flow is most effectively impacted by a diversion angle of 25°. The study provided findings for various discharges flowing (12.3 and 17 L/s); a total of 95 runs were performed, and investigations revealed that the branching discharge depends on several interconnected parameters. It rises with an increase in the depth ratio. In subcritical flow, the main channel always has a lower water depth than the branch channel. The flowing diversion to the branch channel causes a reduction in water depth downstream of the main channel. The study found that the optimal angle for branching was 25°.

Weak nonlinear thermo bioconvection: Heat transfer via artificial neural network

The present study undertakes an analytical exploration of the onset of regular and chaotic thermo-bioconvection in a suspension of gravitactic microorganisms. Additionally, it employs a machine learning approach for numerical computation and prediction of heat transfer rates. The two-dimensional flow governing dynamics are modeled using the Hamilton-Crosser model for microorganisms. The amplitude of convection is determined by solving the cubic Ginzburg-Landau equation derived by applying the Lorenz technique. Stationary curves are plotted with thermal Rayleigh number and wave number, while Nusselt numbers are depicted over a range of time. The onset of chaotic motion is briefly discussed through Lyapunov plots and a bifurcation diagram. Further, to predict the heat transfer rate with multiple interconnected parameters, an artificial neural network is trained with the Levenberg-Marquardt algorithm to understand the underlying patterns of simulated data. The trained neural network is then employed to estimate the Nusselt number for various values of bioconvection Rayleigh number, bioconvection Lewis number, and bioconvection Péclet number. The values obtained from the artificial neural network models are compared with numerical data for validation and are found to be in good agreement. The findings of weak non-linear stability indicate that chaotic motion emerges in the system at the Hopf-Rayleigh number of 24.635, and fast-swimming microorganisms significantly increase the heat transfer rate. The coefficient of correlation is higher than 0.99, supporting the accuracy of developed ANN models to predict the Nusselt number with high accuracy. This observation supports the potential utilization of microbes in heat transfer applications.

Load Frequency Control Strategy of Interconnected Power System Based on Tube DMPC

Solar thermal power generation shares technical characteristics with traditional thermal power generation. This enables rapid adjustment of turbine generator output to meet the demands of the power grid load for frequency modulation. However, fluctuations in light intensity lead to variations in interconnected power system parameters, posing challenges for load frequency control (LFC). In this study, we propose a Robust Distributed Model Predictive Control (RDMPC) method. This method achieves system trajectory tracking by solving the nominal system optimization problem. It also flexibly adjusts the weights of different Tube models to determine the optimal control law using the standard Tube online combination with various gain values. Additionally, we incorporate the states of adjacent areas into the feedback control law to achieve effective coordination between these areas. Using MATLAB/Simulink, we simulated the power system in two areas. Compared to standard Tube DMPC, our proposed algorithm effectively mitigates the impact of light intensity, enhances adjustment speed, reduces frequency fluctuation, and demonstrates superior control effectiveness.

Vertical Interconnection Technology for RF MicroSystem Packaging.

In this work, 1.5-level interconnections of RF microsystems with good RF performance, integration process compatibility, and high reliability are developed to meet the future demand for wireless communication microsystems in the millimeter wave band. Numerical models of 1.5-level interconnections based on solder balls with different diameters are established and analyzed using HFSS. The optimized structure parameters of 0.2 mm diameter Sn96.5Ag3Cu0.5 (SAC305) solder balls and 0.3 mm diameter Sn63Pb37 solder balls are selected for the interconnection between glass micro substrates and silicon micro substrates and between silicon micro substrates and HTCC substrates, respectively. Integration process parameters of the vertical interconnection are optimized. The micro substrate interconnection samples manufactured based on optimized integration methods and parameters show high reliability.

Effects of Magnetized, Chelated Iron Foliage Treatments, and Metal Halide Lamps on Plant Water Structure, Water Vapor Dynamics, and Resilience for Legumes under Water Stress

A greenhouse study was conducted to determine the effects of foliar applications of magnetized, chelated liquid iron fertilizer for increasing the drought tolerance of two legumes. The study objectives were to determine the drought tolerance effects of four treatments on foliar gas exchange, soil moisture, and plant growth for soybean (Glycine max) and velvet bean (Mucuna pruriens) plants. The plant treatments included applications with chelated liquid iron fertilizer (2.5 and 5%) with a conventional boom sprayer, with and without magnets in the spray lines, and metal halide lamps. Three gas exchange measurements were collected before applying the foliage treatments and after two water stress treatments. A foliage and metal halide lamp treatment deactivated or unlinked nine interconnected gas exchange parameters that are correlated with plant defense activities during water stress conditions. The deactivation of interconnected regulatory gas exchange functions improved metabolic efficiency, reduced stress levels, and boosted plant resilience to abiotic stressors. Also, the study findings suggest that the study treatments maintained or increased the level of biologically structured water in plant tissues and vascular systems.

Associating frailty and dynamic dysregulation between motor and cardiac autonomic systems.

Frailty is a geriatric syndrome associated with the lack of physiological reserve and consequent adverse outcomes (therapy complications and death) in older adults. Recent research has shown associations between heart rate (HR) dynamics (HR changes during physical activity) with frailty. The goal of the present study was to determine the effect of frailty on the interconnection between motor and cardiac systems during a localized upper-extremity function (UEF) test. Fifty-six individuals aged 65 or above were recruited and performed the previously developed UEF test consisting of 20-s rapid elbow flexion with the right arm. Frailty was assessed using the Fried phenotype. Wearable gyroscopes and electrocardiography were used to measure motor function and HR dynamics. In this study, the interconnection between motor (angular displacement) and cardiac (HR) performance was assessed, using convergent cross-mapping (CCM). A significantly weaker interconnection was observed among pre-frail and frail participants compared to non-frail individuals (p < 0.01, effect size = 0.81 ± 0.08). Using logistic models, pre-frailty and frailty were identified with sensitivity and specificity of 82%-89%, using motor, HR dynamics, and interconnection parameters. Findings suggested a strong association between cardiac-motor interconnection and frailty. Adding CCM parameters in a multimodal model may provide a promising measure of frailty.

A comprehensive study incorporating structural equation modeling and factor analysis study on the evaluation of performance criteria for infrastructure development projects

Infrastructure development projects are vital for economic growth and societal well-being for every phase of any futuristic growth in the region, city, or country as its backbone in structure development, but their success hinges on effective performance evaluation. The present research employs a thorough methodology that integrates Structural Equation Modeling (SEM) and Factor Analysis decision-making tools to examine the interdependencies and comparative significance of diverse performance standards for infrastructure development initiatives. Research has been carried out through numerous parameters on sustainable dimensions in terms of usage as service providers and users. The user’s effect in understanding the endogenous and exogenous factors like traffic flow restrictions, road conditions in the existing road network, AQI (Air Quality Index), urbanization, Los (Level of Service) for roads, travelers characteristics, and interconnected parameters. To identify important dimensions and latent elements that have a substantial impact on the overall success of the project, factor analysis will be used concurrently. The research attempts to identify hidden variables and complex interactions that influence project outcomes through this dual-method approach. Key findings will illustrate the significance for stakeholders to be involved in the project planning process, to take the environment into account, and to ensure the project’s financial viability. Additionally, the study finds both competitive and complimentary correlations between various performance metrics, reinforcing the significance of an extensive assessment strategy.

Exploring the Underlying Correlation between the Structure and Ionic Conductivity in Halide Spinel Solid-State Electrolytes with Neutron Diffraction.

The development of cutting-edge solid-state electrolytes (SSEs) entails a deep understanding of the underlying correlation between the structure and ionic conductivity. Generally, the structure of SSEs encompasses several interconnected crystal parameters, and their collective influence on Li+ transport can be challenging to discern. Here, we systematically investigate the structure-function relationship of halide spinel LixMgCl2+x (2 ≥ x ≥ 1) SSEs. A nonmonotonic trend in the ionic conductivity of LixMgCl2+x SSEs has been observed, with the maximum value of 8.69 × 10-6 S cm-1 achieved at x = 1.4. The Rietveld refinement analysis, based on neutron diffraction data, has revealed that the crystal parameters including cell parameters, Li+ vacancies, Debye-Waller factor, and Li-Cl bond length assume diverse roles in influencing ionic conductivity of LixMgCl2+x at different stages within the range of x values. Besides, mechanistic analysis demonstrates Li+ transport along three-dimensional pathways, which primarily governs the contribution to ionic conductivity of LixMgCl2+x SSEs. This study has shed light on the collective influence of crystal parameters on Li+ transport behaviors, providing valuable insights into the intricate relationship between the structure and ionic conductivity of SSEs.

Targeted color design of silver-gold alloy nanoparticles.

This research article focuses on the targeted color design of silver-gold alloy nanoparticles (NPs), employing a multivariate optimization approach. NP synthesis involves interconnected process parameters, making independent variation challenging. Data-based property-process relationships are established to optimize optical properties effectively. We define a color target, employ a green chemical co-reduction method at room temperature and optimize process parameters accordingly. The CIEL*a*b* color space (Commission Internationale de l'Éclairage - International Commission on Illumination) and Euclidean distances facilitate accurate color matching to establish the property-process relationship. Concurrently, theoretical Mie calculations explore the structure-property relationship across particle sizes, concentrations, and molar gold contents. The theoretically optimal structure agrees very well with experimental particle structures at the property-process relationship's optimum. The data-driven property-process relationship provides valuable insights into the formation mechanism of a complex particle system, sheds light on the role of relevant process parameters and allows to evaluate the practically available property space. Model validation beyond the original grid demonstrates its robustness, yielding colors close to the target. Additionally, Design of Experiments (DoE) methods reduce experimental work by threefold with slight accuracy trade-offs. Our novel methodology for targeted color design demonstrates how data-based methods can be utilized alongside structure-property relationships to unravel property-process relationships in the design of complex nanoparticle systems and paves the way for future developments in targeted property design.

Performance metrics of doped graphene nanoribbons based nanoscale interconnects: DFT and NEGF analysis

Due to the scaling of devices, the interconnects delay is the most important concern of the current technology, becomes more significant than the IC delay, and leads to overall impact on the performance of the chip. In the present work, doped Zigzag Graphene Nano ribbons (GNR) have been analyzed for the possible nanoscale interconnect applications. The investigation has been performed using the density-functional theory-based ab-initio approach, discussed in terms of structural stability of ribbons, their electronic and transport properties, and the dynamical parameters. Zigzag GNR has been decorated with itssix possible sites along the width, through P and N type impurities, to select the most favorable site for the further analysis. The stability of the systems has been analyzed in terms of formation energy, the electronic properties in terms of band structure and density of states profiles, whereas, the transport properties in terms of transmission spectrum and I–V characteristics. The magnetic inductance, kinetic inductance, electrostatic capacitance, and quantum capacitance have also been calculated as interconnect parameters. The formation energy calculations suggest that the doping at the edge sites of NRs are more favorable. Further, it has been observed that the metallicity of the doped ZGNR enhances with doping, whereas, the I–V characteristics confirms that the N doped ZGNRs have linear current with constant slope. The performance metrics of ZGNRs have also been analyzed using HSPICE, discussed in terms of delay and power delay product, shows that the N doped ZGNRs have lower delay than the other considered counterparts, defends its application as interconnects in the electronic industry.

Development of machine learning algorithm for assessment of heat transfer of ternary hybrid nanofluid flow towards three different geometries: Case of artificial neural network

The focus of this paper revolves around the examination of flow of ternary hybrid nanofluid, specifically the Al2O3–Cu-CNT/water mixture, with buoyancy effect, across three distinct geometries: a wedge, a flat plate, and a cone. The study takes into account the presence of quadratic thermal radiation and heat source/sink of non-uniform nature. To develop the model, the Cattaneo–Christov theory is utilized. The equations governing the flow are solved by applying similarity transformations and employing the "bvp4c function in MATLAB” for numerical analysis and solution. Conventional methods for conducting parametric studies often face challenges in producing significant conclusions owing to the inherent complex form of the model and the method involved. To address the aforementioned issue, this paper explores the potential of machine learning methods to foresee the conduct of the flow characterized by multiple interconnected parameters. By utilizing simulated data, an artificial neural network is trained using the Levenberg-Marquardt algorithm to learn and comprehend the underlying patterns. Subsequently, the trained neural network is employed to estimate the Nusselt number on the surfaces of all three geometries. This approach offers a promising alternative to traditional parametric studies, enabling more precise predictions and insights into the behavior of complex systems. The Nusselt number is highest for THNF flow over the cone. The mean squared error (MSE) values for the ANN algorithm, across all analyzed cases, range from 0 to 0.03972. The findings contribute to an improved understanding of the characteristics and dynamics of ternary hybrid nanofluid flow in various geometries, assisting in the design and optimization of heat transfer systems involving such fluids.

A Transverse Hamiltonian Approach to Infinitesimal Perturbation Analysis of Quantum Stochastic Systems.

This paper is concerned with variational methods for open quantum systems with Markovian dynamics governed by Hudson-Parthasarathy quantum stochastic differential equations. These QSDEs are driven by quantum Wiener processes of the external bosonic fields and are specified by the system Hamiltonian and system-field coupling operators. We consider the system response to perturbations in these operators and introduce a transverse Hamiltonian which encodes the propagation of the perturbations through the unitary system-field evolution. This approach provides an infinitesimal perturbation analysis tool which can be used for the development of optimality conditions in quantum control and filtering problems. As performance criteria, such settings employ quadratic (or more complicated) cost functionals of the system and field variables to be minimized over the energy and coupling parameters of system interconnections. We demonstrate an application of the transverse Hamiltonian variational approach to a mean square optimal coherent quantum filtering problem for a measurement-free field-mediated cascade connection of a quantum system with a quantum observer.

The relationship of political, socio-cultural and value continuums

The article analyzes the transformation processes in the socio-cultural, political and ideological situation in the country and the mass consciousness of the Russian peoples, based on the new state concept on the Fundamentals of State Policy to preserve and strengthen traditional Russian spiritual and moral values (Presidential Decree № 809 of November 9, 2022). It is shown that the dynamics of the interrelated relevant processes are aimed at strengthening national security and strengthening the reliance in the relevant areas on the values of different order - traditional-cultural, spiritual-moral, political-democratic. Much attention in the article is paid to such humanitarian value as human rights (their provision / violation). The empirical basis of the article was the results of the study conducted by the Center for Sociology of Ideological and Socio-Cultural Processes of ISPR FCTAS RAS in the early post-pandemic period, in 2022, in the territories of Moscow, Murmansk, Arkhangelsk oblasts, Nenets Autonomous Okrug. The research toolkit included questions concerning, in particular, the assessment of the political, socio-cultural situation in the country, the provision of social democratic values - human rights and freedoms, security, social protection, etc. In our view, the relationship of policy actors with religion and religious organizations is one of the parameters of interconnection and interpenetration of current socio-cultural and political processes, which act simultaneously as a factor of social stability, as well as a fairly significant source of values. Adherence to traditional confessions increases the orientation towards a positive image of the homeland. The images of the desired future demonstrate sufficiently stable historical and value relationships and do not lead to contradictions, and the multi-confessional Russia, characterized by the existence of a common space of value, the unity of value orientations acts as a cultural basis for national security.

Disrupted white matter network of brain structural connectomes in bipolar disorder patients revealed by q-ball imaging

BackgroundStructural and functional brain changes have been found to be associated with altered emotion and cognition in patients with bipolar disorder (BD). Widespread microstructural white matter abnormalities have been observed using traditional structural imaging in BD. q-Ball imaging (QBI) and graph theoretical analysis (GTA) improve the specificity and sensitivity and high accuracy of fiber tracking. We applied QBI and GTA to investigate and compare the structural connectivity alterations and network alterations in patients with and without BD. MethodsSixty-two patients with BD and 62 healthy controls (HCs) completed a MR scan. We evaluated the group differences in generalized fractional anisotropy (GFA) and normalized quantitative anisotropy (NQA) values by voxel-based statistical analysis with QBI. We also evaluated the group differences in topological parameters of GTA and subnetwork interconnections in network-based statistical analysis (NBS). ResultsThe QBI indices in the BD group were significantly lower than those in the HC group in the corpus callosum, cingulate gyrus, and caudate. The GTA indices indicated that the BD group demonstrated less global integration and higher local segregation than the HC group, but they retained small-world properties. NBS evaluation showed that the majority of the more connected subnetworks in BD occurred in thalamo-temporal/parietal connectivity. ConclusionOur findings supported white matter integrity with network alterations in BD.

Quality prediction and classification of resistance spot weld using artificial neural network with open-sourced, self-executable and GUI-based application tool Q-Check

Optimizing Resistance spot welding, often used as a time and cost-effective process in many industrial sectors, is very time-consuming due to the obscurity inherent within process with numerous interconnected welding parameters. Small changes in values will give effect to the quality of welds which actually can be easily analysed using application tool. Unfortunately, existing software to optimize the parameters are expensive, licensed and inflexible which makes small industries and research centres refused to acquire. In this study, application tool using open-sourced and customized algorithm based on artificial neural networks (ANN) was developed to enable better, fast, cheap and practical predictions of major parameters such as welding time, current and electrode force on tensile shear load bearing capacity (TSLBC) and weld quality classifications (WQC). A supervised learning algorithm implemented in standard backpropagation neural network gradient descent (GD), stochastic gradient descent (SGD) and Levenberg–Marquardt (LM) was constructed using TensorFlow with Spyder IDE in python language. All the display and calculation processes are developed and compiled in the form of application tool of graphical user interface (GUI). Results showed that this low-cost application tool Q-Check based on ANN models can predict with 80% training and 20% test set on TSLBC with an accuracy of 87.220%, 92.865% and 93.670% for GD, SGD and LM algorithms respectively while on WQC 62.5% for GD and 75% for both SGD and LM. It is also expected that tool with flexible GUI can be widely used and enhanced by practitioner with minimum knowledge in the domain.

Cleanroom air quality: combined effects of ventilation rate and filtration schemes in a laboratory cleanroom

ABSTRACT Ventilation performance and air quality in cleanrooms are affected by several interconnected parameters, and a change in one component can impact the entire system. Furthermore, occupant interactions with the physical environment can influence particle dispersion, disrupt current airflow by introducing new wakes and ultimately decrease ventilation efficacy. As a result, we set up an experimental study to measure the effects of traffic, flowrate and filtration on ventilation performance while a source of contamination was inside the room. Experiments included three types of occupant movements (i.e., NM, WO, WT) and were performed under two different airflow conditions. Three different metrics, namely relative ventilation efficiency (), decay rate (R) and exposure (γ), were introduced to statistically compare changes in ventilation performance in response to different experimental setups. Decay rates obtained for 0.3-micron particles decreased by up to 50% in the presence of occupants. Lowering cleanroom flowrate due to additional filtering can reduced ventilation effectiveness by almost 50%. Care should be exercised when changing filter efficiency because it can reduce the rate of air supply. These findings are especially intriguing in the context of cleanroom retrofit, as reducing air exchange rates was an unintended consequence of improving filter efficiency.

Analysis of the planetary mass uncertainties on the accuracy of atmospherical retrieval

Context. Characterising the properties of exoplanet atmospheres relies on several interconnected parameters, which makes it difficult to determine them independently. Planetary mass plays a role in determining the scale height of atmospheres, similarly to the contribution from the average molecular weight of the gas. Analogously, the clouds masking the real atmospheric scale height make it difficult to correctly derive the atmospheric properties. Aims. We investigate the relevance of planetary mass knowledge in spectral retrievals, identifying cases where mass measurements are needed for clear or cloudy and primary or secondary atmospheres, along with the relevant precision, in the context of the ESA M4 Ariel Mission. Methods. We used TauREx to simulate the Ariel transmission spectra of representative targets of the Ariel mission reference sample, assuming different scenarios: a primordial cloudy atmosphere of a hot Jupiter and a hot Neptune, as well as the secondary atmosphere of a super-Earth that also exhibits a cloud presence. We extracted information on the various properties of the atmospheres for the cases of unknown mass or mass with different uncertainties. We also tested how the signal-to-noise ratio impacts atmospheric retrieval for different wavelength ranges. Results. We accurately retrieved the primordial atmospheric composition independently from mass uncertainties for clear atmospheres, while we found that the uncertainties increased for high altitude clouds. We highlight the importance of the signal-to-noise ratio in the Rayleigh scattering region of the spectrum, which is crucial to retrieving the cloud pressure and to accurately retrieving all other relevant parameters. For the secondary atmosphere cases, a mass uncertainty no larger than 50% is sufficient to retrieve the atmospheric parameters, even in the presence of clouds. Conclusions. Our analysis suggests that even in the worst-case scenario, a 50% mass precision level is enough for producing reliable retrievals, while an atmospheric retrieval without any knowledge of a planetary mass could lead to biases in cloudy primary atmospheres as well as in secondary atmospheres.

Abrasive water-jet applications in 6 mm flat glass cuttings

Abrasive Water-Jet Cutting (AWJC) is an unique technique and has found wide applications in the glass cutting industry due to high performance and cost-effective properties. There are several interconnected parameters of great importance in this method. In this experimental study, the selected AWJC parameters are working distances (3, 4 and 5 mm) and feed rates (1000, 1250 and 1500 mm/min) at constant water pressure (140 bar), abrasive grain size (120 mesh) and nozzle diameter (0,76 mm). This study will be concerned with investigating the effects of these parameters on the surface quality of flat glasses. AWJC can improve the surface quality of flat glass, if the cutting parameters are chosen appropriately. It was observed that; the optimum working distance and feed rate parameters should be selected as 3 mm and 1250 mm/min respectively, if minimum grit defect is required. Additionally, 4 mm of working distance and 1000 mm/min of feed rate cutting parameters would generate minimum burr defect.

Quantification of seismic performance factors for modular corner-supported steel bracing system

Modular construction, using steel corner-supported modular units, is an impressive and environmentally friendly construction technique. It leverages off-site factory-controlled fabrication of volumetric units, including beams, columns, braces, and their intra-connections, as well as their on-site assembly through the use of so-called inter-connections, to form a building as a whole. The seismic detailing of both inter-connections and intra-connections deviates from current seismic design provisions, thereby affecting the seismic behaviour of a modular structure as a whole. Despite their widespread application, seismic design and detailing of corner-supported modular structures mostly relies on the available provisions, adopted for traditional lateral load-bearing systems due to their limited research data available. This paper aims to evaluate the seismic performance factors of modular corner-supported steel bracing systems using a rational nonlinear simulation technique. A nonlinear mathematical model which simulates the stiffness/strength deteriorated and pinched hysteretic response of both intra-connection beam-column elements and braced elements is adopted according to available data in literature, whereas the capacity-protected behaviour is assumed for inter-connections. The modelling technique is implemented into nonlinear mathematical models of corner-supported modular steel buildings with four, eight, and twelve storeys. A parametric study is firstly performed to evaluate the influence of inter-connection stiffness parameters on the nonlinear response of the structures. A set of incremental dynamic analyses is secondly conducted to generate data required to quantify seismic performance factors. The results are finally compared with the seismic performance factors recommended by the ASCE/SEI standard for code-approved connections. It is found that a static overstrength factor is close to 2.5 for the four-storey building whereas for taller buildings lower values than 2.5 is achieved. However, the dynamic overstrength factor showing inelastic force redistribution due to dynamic loading achieves values well above 2.5 regardless of building heights. Further, the response modification coefficient of 9.5 is suggested for braced corner supported modular systems which is well above the value for the equivalent code-specified system.