Building Clusters Research Articles

Data driven economic scenarios for retrofitting residential buildings in a northern Italian region

European directives and strategies, such as the ‘European Green Deal’ and the ‘Ren-ovation Wave’, point out the importance of the building sector in achieving the climate goals set by the European Union for 2050. However, a higher renovation rate for the existing buildings is required to achieve these goals. Many barriers prevent the renovation rate from growing. Regarding financial barriers, the long payback times of renovation interventions and the high risk perceived by the potential investors make the renovation rate remain low. Based on data from energy performance certificates, this research proposes a data-driven method to create economic retrofit scenarios for residential buildings using Artificial Intelligence techniques and Monte Carlo simulations. Namely, energy savings have been predicted using an Artificial Neural Network on clusters of residential buildings and the Life Cycle Costs forecasted by Monte Carlo simulations taking into account the uncertainty in many of the inputs. Results obtained by applying the method to a region in northern Italy illustrate two scenarios for the energy retrofit of the built environment, one assuming a payback time of fifteen years and the other of twenty-five years. In both cases, the maximum allowable investment, which varies according to the specific characteristics of the buildings, is much lower than the retrofit costs recorded in the same area in recent years.

CIM-powered physics-based assessment of wind damages to building clusters considering trees

It is important to obtain accurate predictions of wind damage to building clusters for reducing wind-induced economic losses. To this end, a city information model-powered physics-based method for assessing the wind damage to building clusters considering trees was proposed. The influence of trees on wind damage was investigated based on two virtual standard building clusters, revealing that trees can play a shielding role to reduce the risk level of buildings. The reliability and feasibility of the method were demonstrated based on a case study of a manufacturing factory. Moreover, the applicability of the method in city-scale applications was illustrated based on a large urban area covering 619 buildings and 19,740 trees. Based on meteorological forecasting data, the proposed method can help city managers identify and reinforce the building with a high damage risk before strong wind events, thus promoting a safer city.

Empirical mode decomposition of the atmospheric flows and pollutant transport over real urban morphology

The momentum transport and pollutant dispersion in the atmospheric surface layer (ASL) are governed by a broad spectrum of turbulence structures. Whereas, their contributions have not been explicitly investigated in the context of real urban morphology. This paper aims to elucidate the contributions from different types of eddies in the ASL over a dense city to provide the reference of urban planning, realizing more favorable ventilation and pollutant dispersion. The building-resolved large-eddy simulation dataset of winds and pollutants over the Kowloon downtown, Hong Kong, is decomposed into a few intrinsic mode functions (IMFs) via empirical mode decomposition (EMD). EMD is a data-driven algorithm that has been successfully implemented in many research fields. The results show that four IMFs are generally enough to capture most of the turbulence structures in real urban ASL. In particular, the first two IMFs, which are initiated by individual buildings, capture the small-scale vortex packets that populate within the irregular building clusters. On the other hand, the third and fourth IMFs capture the large-scale motions (LSMs) detached to the ground surface that are highly efficient in transport. They collectively contribute to nearly 40% of vertical momentum transport even with relatively low vertical turbulence kinetic energy (TKE). LSMs are long, streaky structures that mainly consist of streamwise TKE components. It is found that the open areas and regular streets promote the portion of streamwise TKE in LSMs, improving the vertical momentum transport and pollutant dispersion. In addition, these streaky LSMs are found to play a crucial role in pollutant dilution in the near field after the pollutant source, while the small-scale vortex packets are more efficient in transport in the mid-field and far-field.

Large-eddy simulation of airflow dynamics around a cluster of buildings

The wind flow around the buildings with different rooftops is studied numerically using large-eddy simulation (LES). The filtered Navier-Stokes equations in LES are used to compute the large eddies, whereas the dynamic Smagorinsky subgrid-scale (SGS) model calculates the small eddies. A turbulent spot method is applied for the synthesis of artificial turbulent fields at the inlet. In this separated and reattached flow, our aim is to analyze the wakes’ vortical structure along with the buildings of different heights and shapes. A large number of engineering applications involve precise predictions of the airflow around buildings to ensure performance and safety. To validate the numerical solver, a comparison is performed with the earlier reported numerical and experimental data. The turbulent flow characteristics are discussed in terms of instantaneous flow structure and time-averaged statistical flow quantities. All the cases are simulated for a Reynolds number [Formula: see text] to understand the turbulent airflow patterns. The flow shows a separable bubble at the leading edge of the rooftop of the building that leads to recirculation at the lee side of the first row of buildings. Due to the recirculation, the suction arises near the leading edge of the building to assure a continuous flow of air around the obstacles. Further, Reynold stresses show high momentum fluxes in the frontal region of the buildings.

Towards Positive Energy Districts: Energy Renovation of a Mediterranean District and Activation of Energy Flexibility

The paper presents the analysis of energy retrofitting, integration of renewable energy and activation of energy flexibility in a cluster of buildings in the surroundings of a port on the Mediterranean Sea in Southern Italy, with the aim of checking the potential for it to achieve the status of positive energy district (PED). The objective of this study is to improve the contemporaneity between local energy generation and energy demand and reduce CO2eq emissions by considering signals that reflect the environmental variability of the electricity grid, through flexibility solutions applied to the HVAC system. The proposed scenarios are based on the dynamic simulation of the district and analyze the effect of actions that activate the energy flexibility of buildings through advanced control strategies of the air conditioning system. The results show that the joint action of energy efficiency strategies, integration of solar energy and energy flexibility improves the environmental sustainability of the district and the balance of energy flows. Specifically, the activation of energy flexibility contributes to a 10% reduction in operational CO2eq emissions and increases in self-consumption of energy per year. The operational emissions of the district vary from the base value of 33.37 tons CO2eq/y to 19.52 tons CO2eq/y in the scenario based on the integration of solar energy systems and energy efficiency measures, and to 17.39 tons CO2eq/y when also the demand-side energy flexibility is activated.

Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster

The use of heat pumps for heating and cooling of buildings is increasing, offering an efficient and eco-friendly thermal energy supply. However, their complexity and system integration require attention to detail, and minor design or operational errors can significantly impact a project’s success. Therefore, it is essential to have a thorough understanding of the system’s intricacies and demands, specifically detailed system knowledge and precise models. In this article, we propose a method using artificial neural networks to develop heat pump models from measured data. The investigation focuses on an operational heat pump plant for heating and cooling a cluster of municipal buildings in Stavanger, Norway. The work showcases that the network configurations can provide process insights and knowledge when detailed system information is unavailable. Model A predicts the heat pump response to temperature setpoint and inlet conditions. Except for some challenges during low-demand cooling mode, the model predicts outlet temperatures with Mean Absolute Percentage Error (MAPE) between 2 and 5% and energy production and consumption with MAPE below 10%. Summarizing the five-minute interval predictions, the model predicts the hourly energy production and consumption with MAPE at 3% or less. Model B predicts energy consumption and coefficient of performance (COP) from measured inlet and outlet conditions with MAPE below 5%. The model may serve as a tool to develop system-specific compressor maps for part-load conditions and for real-time performance monitoring.

Assessing the influence of nonlinear soil behaviour on site-city interaction

Recent studies have confirmed that dense building clusters located in urban areas significantly changed ground motion. This phenomenon, known as site-city interaction (SCI) effects, have received increased attention. However, most of the current studies on the SCI effects were performed by assuming the linear behaviour of soil response, despite the significant influence of soil nonlinearity on the site and structural response. This article investigates the effects of the nonlinear behaviour of soils on SCI effects, through a series of numerical results. A new parameter named PSCI (percentage of SCI effects) was proposed to quantify SCI effects, which presented the percentage of ground motion modification caused by SCI effects. The parameter study revealed a significant influence of soil nonlinearity on SCI effects. The results suggested that soil nonlinearity affected SCI effects mainly through two opposing mechanisms: “damping increase” which reduces SCI effects; “modulus decrease” which increases SCI effects. A relationship between the nonlinearity-induced modification of SCI effects and the degree of nonlinearity was established.

Low-LOD fragility curves of structural units in masonry building clusters for territorial risk analysis

The constant yet irregular construction process taking place in historical towns during decades, if not centuries, is based on spontaneous, and often unlawful, aggregation, superposition, and juxtaposition of building units, realized in different times with different construction techniques and materials. This process produces the so-called masonry building clusters, which constitute the fabric of most historical towns in Southern Europe. Though usually well integrated from the functional standpoint, their structural functioning is often hardly interpretable, especially under seismic actions. Since 2008, the Italian technical code, recognizing the complexity of analyzing masonry building clusters, allows subdividing them into distinct structural units and then by performing separate non-linear analyses on each structural unit. The code provides indications on how to identify each structural unit and, once it has been singled out, modeling and analysis can benefit of some simplifications: each floor can be analyzed and verified separately, the axial force variation in the walls can be disregarded, and the global torsional behavior can be neglected. These common-sense simplifications aim at avoiding complex FEM models, which would overexert the simplifications introduced, and encourage the development of more efficient assessment procedures, especially for large scale territorial risk studies, where the required level of detail (LOD) is generally low. Along these lines, this study proposes a procedure formulated in analytical form, which allows developing fragility curves of structural units belonging to masonry building clusters based, both, on gross geometrical data available from typological surveys, and on typical mechanical properties taken from the Italian code. Recognizing that the analysis tool should be commensurate to the knowledge level, the procedure has been implemented in a spreadsheet. This allows producing, with limited computational effort, fragility curves to be used in comprehensive risk assessment studies of historical centers. An application on a masonry building cluster in Umbria, Italy, is presented, which shows that the procedure yields accurate results with concise input data and that the corresponding fragility curves can be obtained with minimal computational effort, which is an extremely useful feature when performing risk analyses of historical city centers.

Research on energy saving prediction of building clusters based on data mining algorithm

In order to solve the problem of excessive burden of electricity and energy consumption in urban landscape buildings clusters, the study combined data mining algorithms to establish a prediction model for energy-saving renovation of urban landscape building clusters. Firstly, the energy demand and energy consumption of the urban landscape buildings complex were analysed, a mathematical model was established to predict the energy consumption of the building complex. Then, the prediction model of energy-saving retrofitting of building clusters was constructed by combining data mining techniques. The experimental results show that the change trend of total energy consumption is different under different single influencing factors of energy consumption. Among them, the lighting power density factor has the greatest influence on energy consumption, and its annual energy consumption change rate can reach about 0.35. Applying the prediction model to the energy consumption prediction of 15 urban single buildings, it was found that the total energy consumption of the buildings before the retrofit was much higher than that after the retrofit, and the energy-saving rate of the whole observed sample building group was as high as 18.5%, meanwhile, the highest energy-saving rate of the single buildings reached 30.1%.

Building cluster demand flexibility: An innovative characterization framework and applications at the planning and operational levels

Flexible loads in building clusters are a sizable source of demand flexibility that can be tapped to provide grid services. Even with extensive research on demand flexibility characterization, it is not well studied how characterizing demand flexibility can facilitate energy aggregators bidding into wholesale electricity markets. In this paper, we propose an innovative framework that characterizes demand flexibility from a building cluster in a bottom-up manner and generates detailed information that can be used for bidding into the ancillary services market. Our framework allows system operators to perform a comprehensive evaluation of grid service bids submitted by energy aggregators. Different characterization parameters have been suggested depending on temperature setpoint and dead-band schedules. As a computationally efficient representation of a building cluster, we introduce a novel, scalable virtual storage model capable of accounting for temporal changes in temperature setpoints and/or dead-bands. Unlike its existing counterparts, our model is applicable to any building cluster regardless of the level of heterogeneity in building properties. The proposed framework is tested on a cluster consisting of 1000 single-family houses, each equipped with a heat pump as the heating system, controlled by a three-layer hierarchical controller. The results demonstrate that detailed flexibility characterization allows system operators to procure demand-side grid services without thermal comfort being jeopardized or power systems being adversely affected. For the introduced bidding scheme to be viable, the system operator needs to have an accurate estimate of the baseline power trajectory of each aggregator. This is important for compensating aggregators and preventing a malicious one from submitting fraudulent bids.

An Incentives Planning Framework for Residential Energy Retrofits: A Life Cycle Thinking-Based Analysis under Uncertainty

Building energy retrofits can reduce emissions and increase cost savings. Some retrofits that can deliver higher emissions savings are not popular due to a lack of economic justifications. Financial incentives can be used to change buyer perception around such retrofits. This study proposes a framework to identify the best-performing retrofit strategies for a given building cluster and the optimal incentive amounts to promote the chosen strategies, accounting for uncertainties, stakeholder priorities, and budget constraints. The proposed framework was demonstrated using a case study complemented with policy insights. Life cycle cost savings and capital cost significantly impact retrofit purchase decisions. Case study results showed that retrofitting houses heated with electricity can produce significant cost savings. However, adopting energy-conscious behaviours in houses heated with natural gas and injecting renewable natural gas into the gas supply can produce two times more emissions savings achieved by any retrofit strategy applied to an electrically heated house. This indicates the need for adopting performance-based incentives over the prescriptive approach to reward occupant efforts in addition to asset performance. Despite potential life cycle cost savings, incentives must be complemented with low-interest loans to promote retrofit strategies carrying higher capital costs.

Clustering of SARS-CoV-2 in Households in New York City: A Building-Level Analysis, March-December 2020.

To identify the proportion of coronavirus disease 2019 (COVID-19) cases that occurred within households or buildings in New York City (NYC) beginning in March 2020 during the first stay-at-home order to determine transmission attributable to these settings and inform targeted prevention strategies. The residential addresses of cases were geocoded (converting descriptive addresses to latitude and longitude coordinates) and used to identify clusters of cases residing in unique buildings based on building identification number (BIN), a unique building identifier. Household clusters were defined as 2 or more cases within 2 weeks of onset or diagnosis date in the same BIN with the same unit number, last name, or in a single-family home. Building clusters were defined as 3 or more cases with onset date or diagnosis date within 2 weeks in the same BIN who do not reside in the same household. NYC from March to December 2020. NYC residents with a positive SARS-CoV-2 nucleic acid amplification or antigen test result with a specimen collected during March 1, 2020, to December 31, 2020. The proportion of NYC COVID-19 cases in a household or building cluster. The BIN analysis identified 65 343 building and household clusters: 17 139 (26%) building clusters and 48 204 (74%) household clusters. A substantial proportion of NYC COVID-19 cases (43%) were potentially attributable to household transmission in the first 9 months of the pandemic. Geocoded address matching assisted in identifying COVID-19 household clusters. Close contact transmission within a household or building cluster was found in 43% of noncongregate cases with a valid residential NYC address. The BIN analysis should be utilized to identify disease clustering for improved surveillance.

Seismic performance of mat-founded building clusters on liquefiable soils treated with ground densification

Current guidelines for evaluating the performance of ground densification as a liquefaction countermeasure near buildings are based on free-field conditions or, at best, consider one structure experiencing soil-structure interaction (SSI) in isolation. However, in urban areas, where structures are constructed in close vicinity of each other, structure-soil-structure interaction in liquefiable deposits near two (SSSI2) or multiple (≥3) buildings in a cluster (SSSI3+) has been shown as consequential on key engineering demand parameters (EDPs), particularly differential settlement. Furthermore, the potential tradeoffs associated with ground improvement in urban settings, considering SSSI2 and SSSI3+, are currently not well understood or defined. In this paper, three-dimensional (3D), fully-coupled, nonlinear, dynamic finite element analyses are first validated with centrifuge models of SSI and SSSI2, including ground densification. These models are subsequently used to explore the influence of building arrangement (two adjacent structures and four structures in a square block) and spacing on key EDPs for mitigated structures undergoing SSSI2 and SSSI3+ compared to that under isolated SSI. For the conditions evaluated, it is shown that both SSSI2 and SSSI3+ could reduce the average settlement of mitigated structures compared to SSI at building spacings (S) > 0.5Wfnd (where Wfnd is the foundation width), particularly in larger clusters experiencing SSSI3+. On the other hand, both SSSI2 and SSSI3+ amplified the permanent tilt of the mitigated structures compared to SSI at S < 0.5Wfnd. The impact of these interactions on tilt reduced at larger spacings. A limited, subsequent numerical sensitivity study showed that pulse-like input motions together with the stress and flow-path bias introduced by SSSI2 and SSSI3+ can increase the uneven accumulation of soil strains below the mitigated structures compared to cases experiencing SSI or the same building clusters subject to non-pulse-like motions. This led to a greater amplification in tilt of mitigated structures experiencing SSSI2 and SSSI3+ at shorter spacings under the selected pulse-like motions. Overall, the results point to the importance of considering the impact of building cluster arrangement, spacing, soil and structural properties, and ground motion characteristics in the design of ground improvement in urban settings.

A computational framework for the simulation of wind effects on buildings in a cityscape

Central business districts are densely built with clusters of high-rise buildings that are exposed to winds, impacting their strength, serviceability, and habitability. A computational framework to perform the city-scale evaluation of wind effects on buildings was proposed in this work, while existing studies have been mostly limited to isolated building cases. The framework features (1) a GIS-based topology generation of the cityscape, (2) the large eddy simulation (LES) for time-varying wind loads on buildings, (3) a city-scale time history analysis to capture the building cluster response, (4) a high-fidelity visualization of results, and (5) the city-scale performance assessment. A portion of downtown San Francisco is utilized to demonstrate the framework's potential to usher in a new era in the computational design and assessment of buildings for strength, serviceability, and habitability. The proposed framework offers to complement wind tunnel studies as a virtual twin by enhancing the provision to model building cluster dynamics synchronously. The framework is designed to serve as a system-level computation-based analysis and design platform for building clusters, and does not necessarily aim to reflect recent advances in LES modeling. Each component of the framework is modular and its fidelity can be enhanced by introducing any available refinement.

Repurposing a Geothermal Exploration Well as a Deep Borehole Heat Exchanger: Understanding Long-Term Effects of Lithological Layering, Flow Direction, and Circulation Flow Rate

In the drive to achieve net-zero carbon emissions, decarbonisation of heating is essential. This can be facilitated by geothermal energy, but drilling geothermal wells is associated with high risks and costs. The use of preexisting wells (e.g., exhausted hydrocarbon wells or failed geothermal exploration boreholes) offsets this cost while potentially turning liabilities into assets. The Newcastle Science Central Deep Geothermal Borehole (NSCDGB) is a geothermal exploration well that was drilled to target the Carboniferous Fell Sandstone Formation at 1418.5 to 1795 m depth. However, low hydraulic conductivities prevented the development as a conventional “wet” geothermal abstraction well; therefore, new alternative methods of development are being explored. This work investigates the repurposing of the NSCDGB as a deep borehole heat exchanger (DBHE), focusing on the sustainable operation of the system in the long term by employing a constant heat load designed to contribute to local buildings or a heat network. Numerical modelling was undertaken by using OpenGeoSys software to analyse the thermal and hydraulic performance of the system. Both homogeneous and heterogeneous models were developed to compare the influence of lithological layering in contrast to a homogeneous (nonstratified) subsurface geological model. Results from homogeneous simulations modelling the DBHE to a depth of 922 m show that a 50-kW heat load can be supported for a lifetime of 25 years. This corresponds to a 65-kW building load when coupled to a heat pump with a coefficient of performance of 4.33. Thus, the DBHE could meet up to 72% of the heat demand of the adjacent urban sciences building. Rather than being a purely hypothetical case study, this work considers a real existing borehole, adjacent to a building cluster which could make use of the geothermal heat. Heterogeneity, which has been considered for the first time at the NSCDGB site, exhibits a minor impact in comparison to homogeneous simulation results. Flow direction and mass flow rate also exhibited small effects on the system performance, whereas if the exploration well could be repurposed to increased depths, the heat load could be increased. This is the first study of a coaxial DBHE at the NSCDGB site considering long-term effects of mass flow rate, heterogeneity, and flow direction. The study evaluates the feasibility of repurposing an exploratory geothermal well in the UK as a DBHE that can be used as a low-carbon heat source for space heating, thus converting liabilities into potential “green energy” assets.

Holistic management of a smart city thermal energy plant with sewage heat pumps, solar heating, and grey water recycling

This article introduces a modern thermal energy plant consisting of sewage heat pumps, a biogas boiler, thermal solar collectors, and grey water recycling. It further discusses advanced methods to achieve energy efficiency in the plant operation. The project is a collaboration between the industrial plant designer, the municipal plant owner, and the local academic institution. The article presents the framework for the collaboration. The overall target is to investigate how the experience and competence of the three partners can lead to improved operation using data-driven methods and optimization strategies. The industrial partner can closely follow up on its design and increase its knowledge of artificial intelligence and data-driven methods. The municipal partner is given a “free-of-charge” system review. New knowledge and reduced life cycle costs and emissions are possible outcomes. The academic partner gets access to a “living green laboratory,” a unique dataset, and the opportunity to validate developed models and optimization strategies. The plant represents the state-of-the-art for a medium scaled, local thermal energy production system in an existing building cluster. The design energy and emission targets are presented and compared to the operational results. Though the municipal partner can report good agreement between targets and results, an evaluation of the day-to-day operation identified practical examples of system conditions that Artificial Intelligence may improve. The article concludes with a description of plans for future work and a broader discussion of the impacts of introducing data-driven methods to real-life systems.

Empirical seismic vulnerability models for building clusters considering hybrid intensity measures

Seismic risk and vulnerability assessments of buildings are essential to quantify the vulnerability of large-scale urban and rural areas. Regional risk analysis needs a relationship model between ground motion parameters (macroseismic intensities) and structural seismic demands. However, the current practice mainly considers only the impact of horizontal seismic demands, while often neglecting the vertical seismic demands. Additionally, the selection of the macroseismic intensities traditionally depends on the chosen empirical damage assessment method. However, different macroseismic intensities even for the same ground motion might lead to different results. This paper comprehensively considers the impact of multidirectional earthquake sequences, updates the instrument intensity calculation model, and proposes a quantitative method considering hybrid intensity measures to assess the vulnerability of building clusters. The traditional nonlinear regression model is modified and improved using risk analysis, reliability, Bayesian, and probabilist methods. The updated vulnerability model of typical building clusters is then developed based on the hybrid intensity measures. The field inspection database of 57 typical destructive earthquakes in Yunnan, China, is used to update the prediction models for the seismic vulnerability of buildings.

Seismic Fragility Assessment of Inner Peripheries of Italy through Digital Crowd-Sourcing Technologies

The structural and seismic fragility assessment of minor historical centers of the Inner Peripheries of Italy is a key phase of the preservation process of the historical and cultural features of a portion of the Italian building stock, whose reuse is crucial for the reversal of shrinking trends and the stimulation of population growth. In this framework, the opportunities offered by digital crowd-sourcing technologies with respect to performing probabilistic structural safety assessment at a large scale are investigated herein. The objective of this research was to exploit data and information available on the web such that the key building features of an area of interest are collected through virtual inspections, historical databases, maps, urban plans, etc. Thus, homogeneous clusters of buildings identified in the area of interest are catalogued and associated with specific building classes (chosen among those available in the literature), and the buildings’ levels of seismic fragility are determined through the development of fragility curves. The research outcomes show that the proposed approach provides a satisfactory initial screening of the seismic fragility level of an area, thus allowing for the identification of priority zones that require further investigations or structural interventions to mitigate seismic risk.

Developing a multi-level energy benchmarking and certification system for office buildings in a cold climate region

Energy benchmarking is an accurate tool to measure, monitor, and reduce end-use energy consumption in the building sector using comparison scenarios. Various studies have applied Bottom-Up energy consumption assessment to compare the energy performance of a group of buildings with a benchmark. The Bottom-Up method mostly relies on simulating an ideal building to develop a benchmark. However, this study has developed a Top-Down energy benchmarking methodology based on the actual energy consumption within a cluster of governmental office buildings. The method presents multi-level benchmarks to provide a detailed policy for improving the energy efficiency of buildings in the short or long-term. In an exploratory study, 26 office buildings in a cold climate region were investigated to identify the multi-level benchmarks. Four general benchmark levels were developed, including the ‘Best Practice, ‘Good Practice’, ‘Benchmark’, and ‘Poor Practice’. In addition, 15 energy efficiency classes were also introduced and applied as a base for developing the ‘Energy Performance Certificate’ method. The results indicate that the benchmark values for electricity and natural gas are 40 and 252 kWh/m2/yr, respectively, while the total energy benchmark is 292 kWh/m2/yr. According to the benchmarking results, 69% of the case studies were inefficient, 23% were labeled ‘C’, and no cases were labeled ‘A’ or ‘B’. The multi-level benchmarking system can provide quick and clear guidance for building designers, operators, and government regulation/enforcement agencies; thus, it can apply at local, regional, and international levels. These benchmark levels establish reference points for measuring and rewarding good performance, on the other hand, it recognizes poor-performance buildings and prioritizes them for energy efficiency improvement. The method can be replicated in various climates and urban scales.

Seismic responses of near-fault building clusters including in-plane rotational component in investigated lump model of earth medium

An algorithm is proposed for modeling wave propagation with in-plane rotational component in earth medium. The proposed algorithm can not only carry out in-plane rotational wave propagation in half-space medium but also correctly undertake in-plane dynamic moment loading. Connecting ground and structure dynamically, a numerical method is given for implementing translational and rotational wave propagation in building clusters and earth medium caused by fault rupture. Three verification examples’ results show correctness of the proposed algorithm in calculating ground responses due to a single vertical force, dynamic moment loading and finite-fault seismic sources, respectively. The seismic responses of near-fault building clusters are simulated during an Mw6.0 hypothetical earthquake. It is shown that in-plane rotational wave propagation in earth medium may increase significantly structural seismic responses. In all the three building clusters, the structural seismic response most affected by in-plane rotational wave propagation in earth medium is median bending moment (maximal increasing rate up to 163.0%), the second is inter-storey shear force (maximal increasing rate up to 37.5%) and the third is inter-storey drift (maximal increasing rate up to 24.2%). The existence of structural in-plane rotation can increase the horizontal displacements on the roof of buildings on the hanging wall and rupture forward of fault and decrease those on the footwall of fault. Altogether, the each-storey peak earthquake responses of one structure on hanging wall are larger than those of another identical structure on footwall at the same distance from fault-ground intersecting line. The contribution of rotational wave field in earth media and structures to structural responses should be considered in analyzing seismic responses of a city.