Patented AI tool and method for evaluating building quality - Analysis of indoor environment and human comfort a case study

IF 5.9 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Meqdad Hamdan Hasan , Othman S. Alshamrani , Emhiedy S. Gharaibeh
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引用次数: 0

Abstract

Ensuring indoor environmental quality and occupant comfort in buildings is critical to enhancing productivity and well-being, yet existing assessment methods often fail to integrate objective measurements (collected by sensors on an autonomous robot, representing measurable environmental parameters such as air quality and temperature) with subjective feedback (gathered via online surveys). This study addresses this gap by developing an autonomous tool for evaluating the quality of buildings and building systems. The objective is to compare the effectiveness of Bayesian Belief Networks, a novel artificial intelligence-based approach, with a classical Linear Additive Method that incorporates Analytic Hierarchy Process and Multi-Attribute Utility Theory. Data collection is achieved using an autonomous robot for objective measurements and Bluetooth-guided occupant surveys for subjective feedback. Two Bayesian Belief network models and one Linear Additive Method model were developed and evaluated using data from an educational building in the Eastern Province of Saudi Arabia. Results show that while the Bayesian Belief Networks algorithm requires more computational time, it effectively handles complexities in hybrid data and provides more reliable predictions for indoor environmental quality. The comparison revealed that one Bayesian Belief Networks closely matches Linear Additive Method results with a correlation coefficient of 0.92, while the other did not converge, highlighting challenges in model optimization. This novel framework offers significant potential for scalable and accurate building quality assessments across diverse building types and climates.
建筑质量评估的专利人工智能工具和方法——室内环境和人体舒适度分析案例研究
确保建筑物的室内环境质量和居住者舒适度对于提高生产力和福祉至关重要,但现有的评估方法往往无法将客观测量(由自主机器人上的传感器收集,代表可测量的环境参数,如空气质量和温度)与主观反馈(通过在线调查收集)结合起来。本研究通过开发一种评估建筑物和建筑系统质量的自主工具来解决这一差距。目的是比较贝叶斯信念网络的有效性,这是一种基于人工智能的新方法,与经典的线性加性方法结合了层次分析过程和多属性效用理论。数据收集使用自主机器人进行客观测量,并使用蓝牙引导的乘员调查进行主观反馈。利用沙特阿拉伯东部省一座教育大楼的数据,开发并评估了两个贝叶斯信念网络模型和一个线性加性方法模型。结果表明,虽然贝叶斯信念网络算法需要更多的计算时间,但它可以有效地处理混合数据的复杂性,并提供更可靠的室内环境质量预测。对比发现,一种贝叶斯信念网络与线性加性方法的结果接近,相关系数为0.92,而另一种贝叶斯信念网络不收敛,凸显了模型优化方面的挑战。这种新颖的框架为跨不同建筑类型和气候的可扩展和准确的建筑质量评估提供了巨大的潜力。
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来源期刊
Ain Shams Engineering Journal
Ain Shams Engineering Journal Engineering-General Engineering
CiteScore
10.80
自引率
13.30%
发文量
441
审稿时长
49 weeks
期刊介绍: in Shams Engineering Journal is an international journal devoted to publication of peer reviewed original high-quality research papers and review papers in both traditional topics and those of emerging science and technology. Areas of both theoretical and fundamental interest as well as those concerning industrial applications, emerging instrumental techniques and those which have some practical application to an aspect of human endeavor, such as the preservation of the environment, health, waste disposal are welcome. The overall focus is on original and rigorous scientific research results which have generic significance. Ain Shams Engineering Journal focuses upon aspects of mechanical engineering, electrical engineering, civil engineering, chemical engineering, petroleum engineering, environmental engineering, architectural and urban planning engineering. Papers in which knowledge from other disciplines is integrated with engineering are especially welcome like nanotechnology, material sciences, and computational methods as well as applied basic sciences: engineering mathematics, physics and chemistry.
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