In the 1990s, PBD was introduced as a new structural design procedure to meet targeted building performance subject to ground shaking (SEAOC 1995; FEMA 1997). Although the first-generation PBD methods considered actual seismic demand and nonlinear building capacity, they were deterministic in nature. The second generation of performance-based earthquake engineering (PBEE) methodology was proposed to quantify the mean annual rate of exceedance of earthquake impact λ_E(dv) by capturing the uncertainty in ground shaking, building behavior, and decision variables (Cornell and Krawinkler 2000; Porter 2003). The PBEE framework (summarized in Table 1) was put forward by the Pacific Earthquake Engineering Research Center (PEER) (Porter 2003).

The PEER framework has been applied in the seismic design and evaluation of buildings (e.g., O'Reilly and Calvi 2019; Shome et al., 2015; Jayaram et al., 2012; Zareian and Krawinkler 2012; Liel et al., 2011; Goulet et al., 2007). The PEER's triple integral implicitly assumes that damage measure (dm) conditioned-on-engineering demand parameter (edp) is independent of intensity measure (im), and decision variable (dv) conditioned-on-dm is independent of im and edp. The seismic impact quantification is decomposed into subtasks that can be carried out by a different group of experts (Der Kiureghian 2005). This conditional independence of the PEER framework has enabled other researchers to extend it to PBD for fire (e.g., Lange et al., 2014), hurricane (Barbato et al., 2013), tsunami (Attary et al., 2017; Goda et al., 2021), and wind (e.g., Ciampoli et al., 2011; Petrini and Ciampoli, 2012).

Computing the mean annual rate of exceedance of dvs is computationally intensive, and different approximations are proposed. The triple integral in the PEER framework can be computed using computationally intensive Monte Carlo simulations (e.g., Jayaram et al., 2012; Goulet et al., 2007). Different stochastic models, such as Poisson, Markov, semi-Markov, renewal, or trigger type, have been considered for earthquake modeling (Anagnos and Kiremidjian 1984). With Poisson's occurrence of the earthquake load assumption, Der Kiureghian (2005) formulated a closed-form solution of the PEER framework. The closed-form solution of the mean annual rate is identical to the PEER framework. However, when the PEER framework is extended beyond 1 year, it gives a conservative result (Der Kiureghian 2005). Similarly, with Poisson's earthquake arrival assumption, Wen and Kang (2001a) developed a closed-form solution for earthquake load formulated under life cycle cost (LCC) (Table 2). The LCC equation shown in Table 2 is a generalized equation that can be used for earthquake, wind, and earthquake and wind MH. In addition, it accounts for the coincidence rate of earthquake and wind hazard in the calculation of the LCC. Takahashi et al. (2004) considered a renewal model of earthquake occurrences in the LCC analysis. The LCC approach has been used in buildings' seismic design applications (e.g., Wen and Kang 2001b; Liu et al., 2003; Mitropoulou et al., 2011; Castaldo et al., 2016). Mahsuli and Haukaas (2013) proposed a reliability-based approach to solving the loss assessment.

The current wind load design follows the Davenport wind loading chain (Davenport 1967; Isyumov 2012). In the wind loading chain, the wind response of tall buildings is determined by considering local wind climatology, local wind exposure and topography, structural aerodynamic characteristics (governed by building shape), and structural dynamic properties (Kareem et al., 2019; Bezabeh et al., 2020b; Solari 2020). The framework was developed for synoptic and stationary winds. Non-stationarity of the wind load, however, has been identified as an important factor to consider (Kareem and Wu 2013; Solari et al., 2015; Hong 2016). Kareem et al. (2019) generalized the Davenport wind loading chain to account for a non-stationary wind–force–response relationship. Unlike earthquake load, for wind load, the building's aerodynamic interactions are evolving with the change in the built environment (Davenport 1983; Elshaer et al., 2017). Thus, the design for wind loads should account for the evolution of the built environment.

Bezabeh et al. (2020b) have provided a state-of-the-art review on PBD for wind loads. The PEER framework was extended for “Performance-Based Wind Engineering” (PBWE, Table 1, Ciampoli et al., 2011). Different researchers have used the PBWE framework (e.g., Augusti and Ciampoli 2008; Ciampoli et al., 2011; Ciampoli and Petrini 2012; Spence and Kareem 2014; Chuang and Spence 2017; Suksuwan and Spence 2019; Ouyang and Spence 2021). Similar to PBEE, the PBWE framework is computationally intensive and requires quantifying the probabilistic hazard to loss assessment. Wen and Kang (2001a) proposed an LCC-based closed-form solution of the probabilistic wind design framework (Table 2). The LCC framework has been applied for tall building wind load design (e.g., Le and Caracoglia 2021; Micheli et al., 2019, 2021; Cui and Caracoglia 2018, Cui and Caracoglia 2020; Ierimonti et al., 2017; Ierimonti et al., 2018). Bezabeh et al. (2018a, 2018b) extended the Davenport wind loading chain to account for uncertainties and formulated it in a reliability framework.

The wind load design was mainly undertaken for a linear response that will consequently furnish over designed system (Alinejad and Kang, 2020). The consideration of nonlinear wind design is an emerging area (e.g., Alinejad et al., 2020, 2021; Bezabeh et al., 2020b; Elezaby and El Damatty 2020; Huang and Chen 2022). To ameliorate this, the ASCE (2019) pre-standard has put forward a PBWD of buildings for wind load, where both linear elastic and nonlinear time history analysis (NLTHA) can be utilized. Chuang and Spence (2017) presented a wind PBD framework to account both for collapse and non-collapse limit states. Bezabeh et al. (2021a, 2021b) proposed a PBWD for a nonlinear wind design framework. Bezabeh et al. (2020b) proposed using self-centering systems to overcome the progressive unidirectional accumulation of plastic deformations.

For earthquake and wind MH design framework, fragility-based (Zheng et al., 2021; Li et al., 2021; Li et al., 2020), LCC-based (Kleingesinds and Lavan 2021; Kleingesinds et al., 2021; Venanzi et al., 2018; Mahmoud and Cheng 2017; Wen and Kang 2001a; Wen and Kang 2001b), and risk-based (Crosti et al., 2010; Duthinh and Simiu 2010; Suksuwan and Spence 2018; Wang M. et al., 2021; Kwag et al., 2021; Roy et al., 2021; Zheng et al., 2021) framework have been proposed to meet different performance objectives (e.g., serviceability/comfort, life safety).

Wen and Kang (2001a) formulated a generalized LCC framework that considers both correlated and uncorrelated earthquake and wind loads (Table 2). The MH framework assumed that earthquake and wind hazards follow a Poisson model (Wen 1990). The MH PBD framework considers uncertainties in hazard, demand, capacity, and initial construction C _o and damage costs. The earthquake and wind loads vary over time; however, the co-occurrence of the maximum values for earthquake and wind loads is small, and this correlation can be ignored (Wen and Kang 2001a; Wen 2001). Suksuwan and Spence (2018) and Chulahwat and Mahmoud (2017), for example, integrated the PEER earthquake λ E ( d v ) and wind λ W ( d v )   PBD frameworks (Table 1) for earthquake and wind MH design, λ M H ( D V ) , as: λ M H ( d v ) = λ E ( d v ) + λ W ( d v ) (1) With an increasing body of knowledge in the MH design framework, there is no reported study for tall-timber buildings. The MH framework for the tall-timber building is presented in Figure 2.

In 1910, the Seismology Society of America identified three emerging areas (McGuire 2004): 1) earthquake event, 2) associated ground motions, and 3) effect on structures. The three emerging areas are still valid today for innovative building systems to reliably quantify the im and edps. In wind engineering, it has gone through similar evolution with the wind loading chain (e.g., Kareem et al., 2019; Bezabeh et al., 2020b; Solari 2020).

The edps in the PBD framework (Table 1) are structural responses, such as acceleration and inter-story drift ratio (e.g., Tesfamariam and Goda 2015; Cui and Caracoglia 2020), obtained through NLTHA. The site-specific hazard can be undertaken using probabilistic seismic hazard analysis (McGuire 2004; Atkinson and Goda 2013; Bommer and Stafford 2020) framework, considering empirical equations (Shahi and Baker 2011; Stafford 2014) or physics-based (Atkinson and Silva 2000; Yamamoto and Baker 2013) ground motion characterization. Finally, different ground motion selection algorithms are used to carry out the NLTHA (e.g., Bradley et al., 2015; Goda, 2015).

Advances in computational resources have enabled researchers to develop high-resolution coupled physics-based ground motion sources to structural simulation models (Kenawy et al., 2021; McCallen et al., 2021). This eliminates the epistemic uncertainty in quantifying free field and foundation level shaking. This model, however, requires a detailed site-specific source model and is computationally intensive. The computationally intensive PBD simulations can be ameliorated with a cloud-enabled computational platform (Deierlein et al., 2020; Kareem 2020). This might not be suitable for preliminary design iterations and verifications; however, it can be used for final design validation.

Once the im at the site is obtained through the hazard analysis, the im and edp relation is established through fragility curves (e.g., Goda and Tesfamariam 2015; De Risi et al., 2019; Cui and Caracoglia 2020; Le and Caracoglia 2021; Silva et al., 2021). Other important areas that warrant investigation for tall-timber buildings are the effect of long-duration earthquakes (Jennings 1970; Tesfamariam and Goda 2017), mainshock and aftershock earthquake sequences (Goda 2015; Tesfamariam and Goda, 2017; Tesfamariam and Goda 2015), a dependency between edps (Goda and Tesfamariam 2015; De Risi et al., 2019), and directionality of wind loads (e.g., Cui and Caracoglia 2020).

The accuracy of the loss assessment is influenced by the available data and the choices of relevant models and parameters (Hosseinpour et al., 2021; Cremen and Baker 2021; O'Reilly and Calvi 2019; Baker and Cornell 2008). In North America, the current seismic loss assessment has evolved from expert-driven (e.g., ATC 13, ATC 1985) to detailed simulation-based models (HAZUS, FEMA–NIBS 2003; FEMA P58, FEMA 2000). In a case where historical data are scarce, simulation-based methods are viable options (Yang et al., 2009; Zareian and Krawinkler 2012). HAZUS (FEMA–NIBS 2003; Kircher et al., 2006) quantifies the loss assessment using the maximum inter-story drift ratio obtained through simulation. Response of tall buildings are subject to multimodal response, and the loss assessment is better captured using a nonuniform evaluation of loss distribution over the height (Shome and Luco 2010; Shome et al., 2015). FEMA P58 (FEMA 2012; ATC 2018) developed a fragility-based loss assessment tool named performance assessment calculation tool. The performance assessment calculation tool contains a large database consisting of the mean and dispersion values of different consequence functions (repair cost, repair time, casualty, and dollar loss). Aslani and Miranda (2005) proposed a story-based loss assessment by considering the damage, downtime due to business interruption, injuries, and loss of lives. Different authors have now developed simplified story-based loss assessments (e.g., Papadopoulos et al., 2019; Shahnazaryan et al., 2021). Similar trends are followed in the loss assessment under wind load (e.g., Le and Caracoglia 2021; Micheli et al., 2019; Micheli et al., 2021; Cui and Caracoglia 2018, 2020; Ierimonti et al., 2017; Ierimonti et al., 2018).

The current state-of-the-art evaluation and design are moving from loss quantification to post-earthquake recovery, called resiliency (Cimellaro et al., 2010; Cimellaro 2013; McAllister 2016; Almufti and Willford 2021; Furley et al., 2021). A comprehensive resilience rating system, Resilience-Based Earthquake Design Initiative, was developed by Arup (Almufti and Willford 2021). Wilson et al. (2021) implemented loss assessment for CLT building using FEMA P58. Furley et al. (2021) implemented a stochastic model to quantify the resiliency of a two-story self-centering CLT building.

SSI is influenced by the site conditions, foundation embedment, flexibility, and shape on foundation impedance (Stewart et al., 1999; Sotiriadis et al., 2020). This interaction is complex, and it can have both beneficial and detrimental effects on the response (Mylonakis and Gazetas 2000). Low-fidelity spring models (e.g., Stewart et al., 1999; Sotiriadis et al., 2020) and high-fidelity finite element models (e.g., Rahmani et al., 2014; Arboleda-Monsalve et al., 2020; McCallen et al., 2021) have been used for SSI. Low-fidelity, linear, and nonlinear spring models can be used at the foundation of the building structure (e.g., Stewart et al., 1999; Sotiriadis et al., 2020). Lesgidis et al. (2018) proposed frequency- and intensity-dependent spring models for SSI.

The SSI is an important intrinsic source of damping for tall buildings (e.g., Cruz and Miranda, 2017). The SSI will consequently impact the response of tall buildings under earthquake and wind loads. However, the SSI effect is not considered in the current tall-timber building design literature. Liu et al. (2008) showed that for a wind-induced response of tall buildings incorporating tuned mass damper (TMD), neglecting the SSI overestimated the response and underestimated the effectiveness of the TMD. Zhou et al. (2018), for eddy current TMD and wind-load application on tall buildings, showed that, with consideration of SSI, the short return period acceleration response exceeded the human comfort limit states.

Damping mechanisms in tall buildings are associated with intrinsic/inherent (or structural), aerodynamic, hysteretic, and supplemental/additional (Smith et al., 2010; Lago et al., 2018). Factors that contribute to the damping are as follows (Yeh et al., 1971; Cruz and Miranda, 2016, 2017): material, friction between members and connections, structural system and joint types, foundation and soil types, interior partitions, exterior cladding, other nonstructural members, and vibration amplitude.

The damping associated with different mass timber building typologies and connections can be quantified from field measurement (e.g., Smith et al., 2010; Kijewski-Correa and Pirnia, 2007). With in situ ambient vibration measurements, Edskär and Lidelöw (2019) and Reynolds et al. (2016) reported building height and damping relationship (Figure 3). From Figure 3, it is apparent that, as expected, with the increase in building height, the damping values are decreasing. The damping–height empirical equations for steel, concrete, and steel/concrete buildings reported in Smith et al. (2010) are plotted in Figure 3. Overall, both have a similar trend, and some of the timber-building damping values are bounded between the empirical equation for steel and RC damping values. The variability in the damping values for the timber building is high, and this warrants more investigation to understand the causal relation of different explanatory factors. The current analytical studies reported on mass timber building do not consider the SSI. Thus, the response obtained through the in situ measurements and analytical studies can be different (e.g., Edskär and Lidelöw 2017, 2019). Thus, future analytical studies should incorporate the SSI in the damping calculations. The building height and frequency relationship is shown in Figure 4. One of the main explanatory factors for the reduction in damping and frequency can be intrinsic damping (e.g., Tamura and Suganuma 1996; Smith et al., 2010).

Motions of a building, due to earthquake and wind loads, are traditionally controlled through mass and stiffness proportioning. Increasing the stiffness, however, can increase the acceleration demand. In addition, it can reduce the overall seismic energy dissipation capacity with consequent unintended failure of connections and capacity-protected elements (ASCE 2019). Using supplemental energy dissipators, the exceedance of serviceability limit state can be reduced. Figure 5 depicts the high-level category of the different supplemental energy dissipation devices.

The supplemental energy dissipation devices can be categorized as passive, active, semiactive, and hybrid damping systems and seismic isolation systems (Soong and Spencer Jr 2002; Takewaki 2011; Lago et al., 2018; De Domenico et al., 2019; Jafari and Alipour 2021b; Takewaki and Akehashi 2021). Traditional passive control damping, such as TMD and tuned liquid damper, are tuned to the fundamental period of the structure and are not suitable for earthquake response mitigation (Willford et al., 2008; Lago et al., 2018). Under severe earthquake loads, the structural response will undergo yielding and consequent period elongation. On the other hand, metallic damper used for earthquake loads will not be suitable for wind loads, as the serviceability wind loads will not yield metallic dampers (Willford et al., 2008). Viscoelastic dampers (Christopoulos and Montgomery 2013) are attractive damping technology that can be used both for earthquake and wind loads. Under MH design consideration, finding the right damper and location by satisfying the MH performance limit states can be cast as an optimization problem (e.g., Suksuwan and Spence 2018; Roy et al., 2021).

Different papers are published on the application of energy dissipation devices for tall buildings: earthquake (e.g., Christopoulos and Montgomery 2013; Kasagi et al., 2016; Nakamura and Hanzawa 2017; Zhou et al., 2018; Hashizume and Takewaki 2020; Uemura et al., 2021), wind (e.g., Liu et al., 2008; Giaralis and Petrini 2017), and MH (earthquake and wind) (e.g., Roy and Matsagar 2019, 2020; Wang M. et al., 2021; Li et al., 2021) loads. Use of base isolations for tall buildings under earthquake (e.g., Taniguchi et al., 2016; Makita et al., 2018), wind (e.g., Chen and Ahmadi 1992; Vulcano 1998; Cheng et al., 2002), and MH (earthquake and wind) (e.g., Roy et al., 2021) loads are also reported in the literature. Liu et al. (2008) and Zhou et al. (2018), respectively, considered the influence of SSI on TMD and eddy-current TMD on tall building response under wind loads. Façades of buildings often are considered nonstructural elements. Recent innovative connections, however, are paving the way for the potential use of the façades as distributed dampers (Jafari and Alipour 2021a,c).

The application of dampers in timber building is limited (e.g., Huang and Chang 2018; Hashemi et al., 2020). The damping for the mass timber is mostly considered with energy dissipating connectors (e.g., Pu et al., 2016; Fitzgerald et al., 2020). More studies, however, in light timber structures are reported (Bolmsvik and Brandt 2013; Jayamon et al., 2018; Ugalde et al., 2019; Tesfamariam et al., 2021b; Nakamura and Fujii 2021).

The MH design optimization problems are subject to uncertainties both on the demand and capacity (e.g., Rosenblueth 1986; Wen 2001; Franchin 2004; Der Kiureghian and Ditlevsen 2009; Spence and Kareem 2014; Kleingesinds et al., 2021). Different optimization under uncertainty algorithms is proposed. The design optimization, under uncertainty, can be cast under reliability-based design optimization (RBDO) (Aoues and Chateauneuf 2010; Valdebenito and Schuëller 2010; Song et al., 2021) and robust design optimization (RDO) (Chatterjee et al., 2019; Chakraborty et al., 2021; Das et al., 2021) frameworks. Subsequently, the problem is solved using gradient (e.g., Franchin et al., 2018; Kleingesinds and Lavan 2021) or non-gradient (derivate-free) (e.g., Hare et al., 2013; Afshari et al., 2019; Umeura et al., 2021) optimization algorithms. In addition, the design requirements to satisfy both earthquake and wind MH loads can be conflicting, and the problem can be formulated under a multi-objective optimization framework (e.g., Afshari et al., 2019).

High-fidelity and detailed three-dimensional building models can be used for the NLTHA of buildings (e.g., Rinaldin and Fragiacomo 2016; Lu et al., 2018; Wang and Wu 2020; Tesfamariam et al., 2021a). For computationally intensive three-dimensional models or experimental data, however, the use of a physics-informed neural network, surrogate models, and multi-fidelity models can be the future direction (Peherstorfer et al., 2018; Swischuk et al., 2019; Deierlein et al., 2020; Chakraborty 2021; Karniadakis et al., 2021).

The rapid growth of the urban population and associated environmental concerns challenged city planners and developers to consider sustainable and cost-efficient building systems. Mass timbers, such as CLT panels and glulam members, have been used as viable, sustainable tall-timber buildings. The tall-timber buildings, however, are lighter and more flexible, which can make those buildings vulnerable to earthquakes and wind loads. With emerging computational tools and analytical models, carrying out PBD with high-fidelity models is apparent. With the current and future research direction in consideration, in this paper, we carried out a state-of-the-art review on PBD for earthquake, wind, and MH loads. The state-of-the-art review has highlighted the challenge and future direction for tall-timber building, which is summarized below.

• With increasing complexity in the tall-timber buildings, the need for a high-fidelity model and validation through experimental work is apparent. Subsequently, multi-fidelity modeling can be developed for design and optimization.