Many parameters may influence the energy efficiency design of buildings, such as fenestration characteristics like window wall rate (WWR), solar heat gain coefficient (SHGC), and window thermal transmittance (Uw) (Ahn et al. 2016; Feng et al. 2017; Yang et al. 2015). Besides, the materials of the building envelope (Mahar et al. 2020; Mirrahimi et al. 2016; Thomas & Ding 2018), HVAC system (Anna Chatzopoulou et al. 2016; Li et al. 2018), and thermal mass (Reilly & Kinnane 2017), among other factors, may also influence the building energy performance. Also, boundary conditions such as climate or internal heat gains affect the building design. Building energy optimization allows for dealing with the large number and complex interactions of energy-efficiency-related design parameters and boundary conditions to optimize the building energy performance, becoming an essential design approach to increase the buildings' energy efficiency (Harkouss et al. 2018).
Sayadi et al. (2021) optimized the WWR considering the HVAC and lighting energy consumption, different window configurations, and thermal comfort of the building for seven different climates. The authors performed the analysis in three southern, western, and eastern orientations, with the following three cases: the application of automated lighting control, the utilization of windows' horizontal overhang, and the application of automatic blinds to control solar heat gains. They concluded that for warmer climates the shades or blinds are effective in reducing the energy consumption in the different window configurations, and the WWR optimal values were reduced compared to the colder climates to reduce solar heat gains. Otherwise, they found that no blind or shades were implemented during the whole-year in colder climates. Also, they observed a wider range of optimal WWR in colder climates.Vera et al. (2017) performed an optimization on fixed exterior complex fenestration systems to improve both visual comfort and energy performance. They used a hybrid genetic algorithm to explore different configurations of the exterior complex fenestration, considering factors such as daylight autonomy, glare index, and energy consumption. Their results showed that it is possible to achieve a balance between visual comfort and energy efficiency through careful optimization of the exterior complex fenestration system design.Jia et al. (2021) studied the application of phase change materials (PCM) on prefabricated constructions to obtain optimal energy savings in five climates in China. They obtained different energy savings depending on the PCM's location and climate analyzed. For example, adding PCM into the walls and roofs in cold climates achieves energy savings of 17.7% and 14.5%, respectively. In mild climates, the impact of PCM is more significant with reductions of 77.1% and 67.5% for PCM located in walls and roof respectively.Ascione et al. (2019) performed a multi-objective optimization to find optimal solutions that produce the Pareto minimization of primary energy consumption, global cost, and discomfort hours in four Italian climates. The study was performed under two approaches: the nearly zero-energy buildings and a cost-optimal approach that minimizes the general costs. Their Pareto curve showed an interaction between the energy-efficient optimal and the cost-optimal in the different climate zones.Delgarm et al. (2016) proposed a methodology for the optimization of building energy efficiency in Iran to evaluate the capability and effectiveness of the methodology in a single room optimizing several parameters such as the building orientation, the shading overhang dimensions, the window size, and the glazing and the wall material properties regarding building energy consumption. As expected, different climates showed a considerable effect on energy consumption. For example, the optimum cooling energy consumption increased from 1 to 5.43 GJ, and the optimum heating energy consumption decreased from 12 to 2.2 GJ from cold to warm climates (Delgarm et al. 2016). The literature agrees that optimal building energy design is highly affected by climate, especially in residential buildings that show low internal heat gains.
The geographical location not only defines the climatic conditions in a building energy optimization but also plays a crucial role in defining structural requirements such as snow, wind, and seismic loads. Especially the latter becomes critical in earthquake-prone regions for light-frame timber buildings because the lateral stiffness of this kind of building is remarkably lower than other structural systems such as reinforced concrete buildings. For example, lateral inter-story drifts often become the governing structural design parameter (Alarcón et al. 2023). A few authors investigated the integration of building energy performance under different seismic conditions. Pohoryles et al. (2020) simulated the effect of the building renovations for structural retrofitting and energy performance under a monetary metric called expected annual loss, which considered the energy consumption and the utilization of fragility curves for the expected damages under a seismic hazard. Twenty European cities located in areas of different climatic conditions and seismic hazards were studied. When using a combined annual loss, significant improvements leading to average reductions of at least one category in terms of combined seismic and energy classes for the residential building studies cases were obtained. Nevertheless, this study only focused on renovating the walls regarding thermal insulation, maintaining all other envelope characteristics such as fenestrations, thermal mass, etc. A similar study was performed by Manfredi & Masi (2018), as they assessed the influence between the seismic performance of infill masonry wall retrofitting techniques in residential buildings and the related energy reductions for each improvement. They identified the most common climates and compared the seismic capacity and hazard demand for the different seismic intensity levels. Their study presented two different rehabilitation configurations, first replacing the external layer with a new improve panel with better thermal insulation properties, and secondly, adding a reinforcement concrete frame to the existing structure similar to a double skin façade. They concluded that reductions up to 31 kWh/m2·year were feasible under the first rehabilitation configuration and still achieved an improvement in seismic performance.
The interaction of energy and seismic requirements may become even more relevant for timber buildings. For instance, timber buildings typically weigh one to two-thirds the weight of concrete buildings, which reduces the thermal inertia, thus increasing the overheating risk (Dietz et al. 2020; Guindos 2019). The lightweight of timber-buildings accentuates the relative importance of the non-structural elements' weight on the buildings seismic mass such as the materials' weight used for the buildings' thermal and acoustic insulation or additional layers for increasing thermal mass (i.e. incorporating phase change materials, concrete floor topping layer). Guindos (2019) reported that adding a concrete topping layer on slabs to improve the thermal inertia of light-frame timber-buildings can increase up to 40% of the seismic mass and the structural forces that the building must withstand. In addition, the low lateral stiffness of timber frame buildings requires high-density shear walls to comply with structural building codes with maximum inter-story drifts (Berwart et al. 2022; Casagrande & Piazza Roberto Tomasi 2014; Ugalde et al. 2019). Such requirement of high-density shear walls also constitutes a design trade-off with other design variables such as the windows openings for energy and visual comfort. Furthermore, it is essential to prevent overturning or rocking for timber buildings under the action of lateral loads. Despite the shear walls provide enough lateral stiffness to be code-compliant, lateral drifts may exceed limits by just the overturning of shear walls. The amount of overturning is significantly larger in slender wall segments in comparison with long segments. In addition, overturning is also strongly affected by the number of stories. Thus, more stories cause larger overturning moments (Berwart et al. 2022). In timber buildings, overturning is prevented by installing lateral connector systems (anchorages) that restrain the uplift at the ends of shear walls as shown in Fig. 1 for typical light-frame timber shear wall configurations. Two of the most used anchorage systems consist of the continuous steel rod system (ATS) and the discrete hold-down systems (HD). The ATS system is typically stronger and stiffer than discrete hold-down systems but it is significantly more expensive, and therefore becoming a critical aspect of the buildings' design (Bagheri & Doudak 2020; Estrella et al. 2021).
In summary, the interaction aspects of the energy and seismic design in light-frame timber-buildings typically included the building mass and the shear wall participation. These two characteristics influence the window openings, overturning risk due to increment in story numbers, and selection of lateral anchorage systems.
Despite the strong energy-seismic design interaction, the optimization of both aspects has been scarcely investigated in timber buildings. OnlyPolastri et al. (2016) analyzed the increment of thermal inertia and its effect on the structural and thermal performance of Cross Laminated Timber and light-frame timber buildings under different connectors for three and five stories. This study was part of an Italian project called TIMBEEST, in which the structural and energy performances were simulated in the second and third phases of the project. The project's second phase consisted of a parametric structural analysis of the connectors, number of stories, and improvement of walls according to Eurocode 8(EN 1998-1 2004) and an elastic horizontal ground acceleration response spectrum variation. The structural results were considered for energy analysis, and a parametric study was performed using TRNSYS(Klein 2018) for both constructive systems and different window configurations considering WWR, SHGC, area, and U-value. Different thermal properties were improved for the increment in the thermal inertia of the building, such as thermal transmittance, periodic thermal transmittance, time shift, decrement factor, internal areal heat capacity, and long-term thermal capacity.Polastri et al. (2016) concluded that the mass increment in both construction systems was feasible in three stories for all Italian seismic zones. Whereas, the mass increase in walls could be accomplished only in zones with low earthquake risk in five-story buildings (Polastri et al. 2016)
As mentioned in the literature, the passive energy design of buildings and structural loads is affected by the locations, and energy-seismic designs strongly interact with each other, especially in timber buildings due to their inherent lightweight and low lateral stiffness. The research about the interaction of the structural and energy behavior on building is low, especially on how the design location can affect the design variables of a timber building. Therefore, the main objective of this research is to analyze and quantify the effect of climates, seismic loads, lateral anchorage, and story number on the optimal energy design solutions, including the seismic behavior in a light-frame timber building. This will allow a better understanding of the linkage among the energy and structural design parameters and how they affect the optimal solutions of lightweight timber-frame multifamily buildings.
Although our study is conducted at a national level, given the broad climatic and seismic conditions of the country, 25 types of climates (Sarricolea et al. 2017), three seismic zones from 0.2g up to 0.4g design accelerations, and six types of soil quality according to the Chilean standard NCh 433 (INN 2012), our results and conclusions provide a valuable insight to the international community of how relevant this interaction becomes in timber buildings. The optimization process was conducted based on a previous optimization model published by the authors, which details are briefly explained in the following section. Still, all the implementation details are found in Wenzel et al. (2022).