Accurate and rapid predictions of residential building performance are crucial for both new building designs and existing building renovations.This study develops an integrated prediction model using a stacking ensemb...Accurate and rapid predictions of residential building performance are crucial for both new building designs and existing building renovations.This study develops an integrated prediction model using a stacking ensemble learning algorithm to predict daylighting,thermal comfort,and energy consumption in residential buildings.The model incorporates multimodal residential building information as inputs,including image-based floorplans and vector-based building parameters.A comparative analysis is presented to evaluate the prediction performance of the proposed stacking ensemble learning algorithm against three base models:Resnet-50,Inception-V4,and Vision Transformer(ViT-32).The results indicated that the stacking ensemble learning algorithm outperforms the base models,reducing the mean absolute percentage error(MAPE)by 0.17%–1.94%and the coefficient of variation root mean square error(CV-RMSE)by 0.37%–2.06%for daylighting metrics;the MAPE by 0.63%–4.46%and the CV-RMSE by 0.62%–5.13%for thermal comfort metrics;the MAPE by 1.42%–6.43%and the CV-RMSE by 0.27%–5.09%for energy consumption metrics of the testing dataset.Further prediction error analyses also indicate that the stacking ensemble learning algorithm consistently yields smaller prediction errors across all performance metrics compared to the three base models.In addition,this study compares the stacking ensemble learning algorithm to traditional machine learning models in terms of prediction accuracy,robustness,and generalization ability,highlighting the advantages of the stacking ensemble learning algorithm with image-based inputs.The proposed stacking ensemble learning algorithm demonstrates superior accuracy,stability,and generalizability,offering valuable and practical design support for building design and renovation processes.展开更多
In the past decade,there has been an increasing recognition of the role of computational design optimization in early-stage performance-based architectural design exploration.However,it remains challenging for designe...In the past decade,there has been an increasing recognition of the role of computational design optimization in early-stage performance-based architectural design exploration.However,it remains challenging for designers to apply such optimization-based design explorations in practice.To address this issue,this paper introduces a design tool,called EvoMass,and an associated design method that facilitates design exploration for building massing typologies in performance-based design tasks.EvoMass is capable of offering architects design options reflecting performance-related building massing typologies for the design task,without necessitating advanced computational design skills.More importantly,it can provide architects with insights into the underlying performance implications,thereby enhancing early-stage performance-based design exploration.EvoMass and its associated design method overcome the limitation in the conventional typology-first-optimization-second design procedure adopted by most existing tools,and it promotes a typology-oriented design exploration method of using computational optimization in performance-based architectural design.To demonstrate the efficacy of EvoMass,case studies derived from architectural design studio tasks,incorporating daylighting,solar exposure,and subjective design intents,and the result of a user survey are presented,which highlights how EvoMass and the performance-based design optimization and exploration can enable architects to achieve a more performance-aware design.展开更多
To address the incomplete problem in pulmonary parenchyma segmentation based on the traditional methods, a novel automated segmentation method based on an eight- neighbor region growing algorithm with left-right scann...To address the incomplete problem in pulmonary parenchyma segmentation based on the traditional methods, a novel automated segmentation method based on an eight- neighbor region growing algorithm with left-right scanning and four-corner rotating and scanning is proposed in this pa- per. The proposed method consists of four main stages： image binarization, rough segmentation of lung, image denoising and lung contour refining. First, the binarization of images is done and the regions of interest are extracted. After that, the rough segmentation of lung is performed through a general region growing method. Then the improved eight-neighbor region growing is used to remove noise for the upper, mid- dle, and bottom region of lung. Finally, corrosion and ex- pansion operations are utilized to smooth the lung boundary. The proposed method was validated on chest positron emis- sion tomography-computed tomography （PET-CT） data of 30 cases from a hospital in Shanxi, China. Experimental results show that our method can achieve an average volume overlap ratio of 96.21 ± 0.39% with the manual segmentation results. Compared with the existing methods, the proposed algorithm segments the lung in PET-CT images more efficiently and ac- curately.展开更多
In performance-based architectural design optimization, the design of building massings and façades is commonly separated, which weakens the effectiveness in performance improvement. In response, this study propo...In performance-based architectural design optimization, the design of building massings and façades is commonly separated, which weakens the effectiveness in performance improvement. In response, this study proposes a hybrid massing-façade integrated design generation and optimization workflow to integrate the two elements in an evolutionary design process. Compared with the existing approaches, the proposed workflow emphasizes the diversity of building design generation, with which various combinations of building massing forms and façade patterns can be systematically explored. Two case studies and a corresponding comparison study are presented to demonstrate the efficacy of the proposed workflow. Results show that the optimization can produce designs coupling the potential of building massings and façades in performance improvement. In addition, the optimization can provide information that supports early-stage architectural design exploration. Such information also enables the architect to understand the performance implications associated with the synergy of building massing and façade design.展开更多
基金funded by the National Natural Science Foundation of China(52178017)the International Science and Technology Cooperation Fund Project(SJXTGJ2104).
文摘Accurate and rapid predictions of residential building performance are crucial for both new building designs and existing building renovations.This study develops an integrated prediction model using a stacking ensemble learning algorithm to predict daylighting,thermal comfort,and energy consumption in residential buildings.The model incorporates multimodal residential building information as inputs,including image-based floorplans and vector-based building parameters.A comparative analysis is presented to evaluate the prediction performance of the proposed stacking ensemble learning algorithm against three base models:Resnet-50,Inception-V4,and Vision Transformer(ViT-32).The results indicated that the stacking ensemble learning algorithm outperforms the base models,reducing the mean absolute percentage error(MAPE)by 0.17%–1.94%and the coefficient of variation root mean square error(CV-RMSE)by 0.37%–2.06%for daylighting metrics;the MAPE by 0.63%–4.46%and the CV-RMSE by 0.62%–5.13%for thermal comfort metrics;the MAPE by 1.42%–6.43%and the CV-RMSE by 0.27%–5.09%for energy consumption metrics of the testing dataset.Further prediction error analyses also indicate that the stacking ensemble learning algorithm consistently yields smaller prediction errors across all performance metrics compared to the three base models.In addition,this study compares the stacking ensemble learning algorithm to traditional machine learning models in terms of prediction accuracy,robustness,and generalization ability,highlighting the advantages of the stacking ensemble learning algorithm with image-based inputs.The proposed stacking ensemble learning algorithm demonstrates superior accuracy,stability,and generalizability,offering valuable and practical design support for building design and renovation processes.
基金supported by the Xi’an Jiaotong-Liverpool University Research Development Fund(RDF-23-01-107)the National Natural Science Foundation of China(52178017)。
文摘In the past decade,there has been an increasing recognition of the role of computational design optimization in early-stage performance-based architectural design exploration.However,it remains challenging for designers to apply such optimization-based design explorations in practice.To address this issue,this paper introduces a design tool,called EvoMass,and an associated design method that facilitates design exploration for building massing typologies in performance-based design tasks.EvoMass is capable of offering architects design options reflecting performance-related building massing typologies for the design task,without necessitating advanced computational design skills.More importantly,it can provide architects with insights into the underlying performance implications,thereby enhancing early-stage performance-based design exploration.EvoMass and its associated design method overcome the limitation in the conventional typology-first-optimization-second design procedure adopted by most existing tools,and it promotes a typology-oriented design exploration method of using computational optimization in performance-based architectural design.To demonstrate the efficacy of EvoMass,case studies derived from architectural design studio tasks,incorporating daylighting,solar exposure,and subjective design intents,and the result of a user survey are presented,which highlights how EvoMass and the performance-based design optimization and exploration can enable architects to achieve a more performance-aware design.
文摘To address the incomplete problem in pulmonary parenchyma segmentation based on the traditional methods, a novel automated segmentation method based on an eight- neighbor region growing algorithm with left-right scanning and four-corner rotating and scanning is proposed in this pa- per. The proposed method consists of four main stages： image binarization, rough segmentation of lung, image denoising and lung contour refining. First, the binarization of images is done and the regions of interest are extracted. After that, the rough segmentation of lung is performed through a general region growing method. Then the improved eight-neighbor region growing is used to remove noise for the upper, mid- dle, and bottom region of lung. Finally, corrosion and ex- pansion operations are utilized to smooth the lung boundary. The proposed method was validated on chest positron emis- sion tomography-computed tomography （PET-CT） data of 30 cases from a hospital in Shanxi, China. Experimental results show that our method can achieve an average volume overlap ratio of 96.21 ± 0.39% with the manual segmentation results. Compared with the existing methods, the proposed algorithm segments the lung in PET-CT images more efficiently and ac- curately.
文摘In performance-based architectural design optimization, the design of building massings and façades is commonly separated, which weakens the effectiveness in performance improvement. In response, this study proposes a hybrid massing-façade integrated design generation and optimization workflow to integrate the two elements in an evolutionary design process. Compared with the existing approaches, the proposed workflow emphasizes the diversity of building design generation, with which various combinations of building massing forms and façade patterns can be systematically explored. Two case studies and a corresponding comparison study are presented to demonstrate the efficacy of the proposed workflow. Results show that the optimization can produce designs coupling the potential of building massings and façades in performance improvement. In addition, the optimization can provide information that supports early-stage architectural design exploration. Such information also enables the architect to understand the performance implications associated with the synergy of building massing and façade design.
