A case-based reasoning approach to improve risk identification in construction projects

Risk management is an important process to enhance the understanding of the project so as to support decision making. Despite well established existing methods, the application of risk management in practice is frequently poor. The reasons for this are investigated as accuracy, complexity, time and cost involved and lack of knowledge sharing. Appropriate risk identification is fundamental for successful risk management. Well known risk identification methods require expert knowledge, hence risk identification depends on the involvement and the sophistication of experts. Subjective judgment and intuition usually from par1t of experts’ decision, and sharing and transferring this knowledge is restricted by the availability of experts. Further, psychological research has showed that people have limitations in coping with complex reasoning. In order to reduce subjectivity and enhance knowledge sharing, artificial intelligence techniques can be utilised. An intelligent system accumulates retrievable knowledge and reasoning in an impartial way so that a commonly acceptable solution can be achieved. Case-based reasoning enables learning from experience, which matches the manner that human experts catch and process information and knowledge in relation to project risks. A case-based risk identification model is developed to facilitate human experts making final decisions. This approach exploits the advantage of knowledge sharing, increasing confidence and efficiency in investment decisions, and enhancing communication among the project participants

Automated mass appraisal system with cross-city evaluation capability: a test development in China

The appraisal of property value is extremely important in a modern economy. For example, developers and end-consumers use appraisals for their investment decisions. Governments use it for taxation purposes, while banks rely on appraisals to update their risk profile when managing mortgage and credit application activities. With fast developing economies, quickly valuing new cities and suburbs as they get built becomes particularly difficult. Globalisation has also increased the need for common international valuation standards and automated methods. This research investigates the present mass appraisal systems and the role of automated valuation models. Financial institutions and institutional investors are increasingly more concerned about constantly updating their present portfolio value especially in a dynamic market. Trends of significant peaks and troughs need to be accounted in a faster cycle time with short bursts of pricing adjustments. The problem poses a challenge because property transactions are infrequently traded unlike other commodities such as securities. Hence, there are not many recent transactions for the same property to receive an updated value with a simple adjustment based on economic conditions. The study proposes a method that solves both large-scale mass appraisal with an ability to search across cities to discover properties with similar characteristics for its update and comparison scheme. This research advances the automated valuation model for the residential property market with a test development performed in China. In particular, the resulting model was tested with data from Chinese Tier 1, 2 and 3 Cities to evaluate property values. This research performs several major accomplishments. First, it demonstrates the efficacy of reducing human cognitive effort in the mass appraisal exercise. Second, by applying Artificial Neural Network capabilities in the automated valuation model, pricing of residential properties are able to draw upon knowledge from more mature cities with greater number of transactions and apply to newer developments in less developed cities. Third, the proposed mass appraisal system shows the reliability and robustness that matches the rapid development of Chinas real estate market that had been verified by a real application. Finally, the approach developed provides a valuable new method for property valuation that reduces the possible bias, increases consistency and lowers the effort required by current manual methods, with a lower data requirement

Optimisation and Decision Support during the Conceptual Stage of Building Design

Merged with duplicate record 10026.1/726 on 28.02.2017 by CS (TIS)Modern building design is complex and involves many different disciplines operating in a fragmented manner. Appropriate computer-based decision support (DS) tools are sought that can raise the level of integration of different activities at the conceptual stage, in order to help create better designs solutions. This project investigates opportunities that exist for using techniques based upon the Genetic Algorithm (GA) to support critical activities of conceptual building design (CBD). Collective independent studies have shown that the GA is a powerful optimisation and exploratory search technique with widespread application. The GA is essentially very simple yet it offers robustness and domain independence. The GA efficiently searches a domain to exploit highly suitable information. It maintains multiple solutions to problems simultaneously and is well suited to non-linear problems and those of a discontinuous nature found in engineering design. The literature search first examines traditional approaches to supporting conceptual design. Existing GA techniques and applications are discussed which include pioneering studies in the field of detailed structural design. Broader GA studies are also reported which have demonstrated possibilities for investigating geometrical, topological and member size variation. The tasks and goals of conceptual design are studied. A rationale is introduced, aimed at enabling the GA to be applied in a manner that provides the most effective support to the designer. Numerical experiments with floor planning are presented. These studies provide a basic foundation for a subsequent design support system (DSS) capable of generating structural design concepts. A hierarchical Structured GA (SGA) created by Dasgupta et al [1] is investigated to support the generation of diverse structural design concepts. The SGA supports variation in the size, shape and structural configuration of a building and in the choice of structural frame type and floor system. The benefits and limitations of the SGA approach are discussed. The creation of a prototype DSS system, abritrarily called Designer-Pro (DPRO), is described. A detailed building design model is introduced which is required for design development and appraisal. Simplifications, design rationale and generic component modelling are mentioned. A cost-based single criteria optimisation problem (SCOP) is created in which other constraints are represented as design parameters. The thesis describes the importance of the object-oriented programming (OOP) paradigm for creating a versatile design model and the need for complementary graphical user interface (GUI) tools to provide human-computer interaction (HCI) capabilities for control and intelligent design manipulation. Techniques that increase flexibility in the generation and appraisal of concept are presented. Tools presented include a convergence plot of design solutions that supports cursor-interrogation to reveal the details of individual concepts. The graph permits study of design progression, or evolution of optimum design solutions. A visualisation tool is also presented. The DPRO system supports multiple operating modes, including single-design appraisal and enumerative search (ES). Case study examples are provided which demonstrate the applicability of the DPRO system to a range of different design scenarios. The DPRO system performs well in all tests. A parametric study demonstrates the potential of the system for DS. Limitations of the current approach and opportunities to broaden the study form part of the scope for further work. Some suggestions for further study are made, based upon newly-emerging techniques

Case Retrieval Nets as a Model for Building Flexible Information Systems

Im Rahmen dieser Arbeit wird das Modell der Case Retrieval Netze vorgestellt, das ein Speichermodell für die Phase des Retrievals beim fallbasierten Schliessen darstellt. Dieses Modell lehnt sich an Assoziativspeicher an, insbesondere wird das Retrieval als Rekonstruktion des Falles betrachtet anstatt als eine Suche im traditionellen Sinne. Zwei der wesentlichen Vorteile des Modells sind Effizienz und Flexibilität: Effizienz beschreibt dabei die Fähigkeit, mit grossen Fallbasen umzugehen und dennoch schnell ein Resultat des Retrievals liefern zu können. Im Rahmen dieser Arbeit wird dieser Aspekt formal untersucht, das Hauptaugenmerk ist aber eher pragmatisch motiviert insofern als der Retrieval-Prozess so schnell sein sollte, dass der Benutzer möglichst keine Wartezeiten in Kauf nehmen muss. Flexibilität betrifft andererseits die allgemeine Anwendbarkeit des Modells in Bezug auf veränderte Aufgabenstellungen, auf alternative Formen der Fallrepräsentation usw. Hierfür wird das Konzept der Informationsvervollständigung diskutiert, welches insbesondere für die Beschreibung von interaktiven Entscheidungsunterstützungssystemen geeignet ist. Traditionelle Problemlöseverfahren, wie etwa Klassifikation oder Diagnose, können als Spezialfälle von Informationsvervollständigung aufgefasst werden. Das formale Modell der Case Retrieval Netze wird im Detail erläutert und dessen Eigenschaften untersucht. Anschliessend werden einige möglich Erweiterungen beschrieben. Neben diesen theoretischen Aspekten bilden Anwendungen, die mit Hilfe des Case Retrieval Netz Modells erstellt wurden, einen weiteren Schwerpunkt. Diese lassen sich in zwei grosse Richtungen einordnen: intelligente Verkaufsunterstützung für Zwecke des E-Commerce sowie Wissensmanagement auf Basis textueller Dokumente, wobei für letzteres der Aspekt der Wiederbenutzung von Problemlösewissen essentiell ist. Für jedes dieser Gebiete wird eine Anwendung im Detail beschrieben, weitere dienen der Illustration und werden nur kurz erläutert. Zuvor wird allgemein beschrieben, welche Aspekte bei Entwurf und Implementierung eines Informationssystems zu beachten sind, welches das Modell der Case Retrieval Netze nutzt.In this thesis, a specific memory structure is presented that has been developed for the retrieval task in Case-Based Reasoning systems, namely Case Retrieval Nets (CRNs). This model borrows from associative memories in that it suggests to interpret case retrieval as a process of re-constructing a stored case rather than searching for it in the traditional sense. Tow major advantages of this model are efficiency and flexibility: Efficiency, on the one hand, is concerned with the ability to handle large case bases and still deliver retrieval results reasonably fast. In this thesis, a formal investigation of efficiency is included but the main focus is set on a more pragmatic view in the sense that retrieval should, in the ideal case, be fast enough such that for the users of a related system no delay will be noticeable. Flexibility, on the other hand, is related to the general applicability of a case memory depending on the type of task to perform, the representation of cases etc. For this, the concept of information completion is discussed which allows to capture the interactive nature of problem solving methods in particular when they are applied within a decision support system environment. As discussed, information completion, thus, covers more specific problem solving types, such as classification and diagnosis. The formal model of CRNs is presented in detail and its properties are investigated. After that, some possible extensions are described. Besides these more theoretical aspects, a further focus is set on applications that have been developed on the basis of the CRN model. Roughly speaking, two areas of applications can be recognized: electronic commerce applications for which Case-Based Reasoning may provide intelligent sales support, and knowledge management based on textual documents where the reuse of problem solving knowledge plays a crucial role. For each of these areas, a single application is described in full detail and further case studies are listed for illustration purposes. Prior to the details of the applications, a more general framework is presented describing the general design and implementation of an information system that makes uses of the model of CRNs

Automated valuation model untuk estimasi nilai pasar rumah berbasis jaringan saraf tiruan backpropagation

Kebutuhan akan rumah tinggal yang meningkat tentunya berpengaruh kepada peningkatan jumlah transaksi rumah di berbagai wilayah. Setiap wilayah tentunya memiliki nilai tersendiri berdasarkan karakteristik tertentu yang menjadi acuan sebagai tempat tinggal. Penilaian nilai pasar rumah (Appraisal) menjadi hal yang penting ketika pemilik rumah hendak melakukan penjualan properti dengan nilai sesuai dengan standar yang berlaku. Computer-assisted Mass Appraisal (CAMA) mengalami perkembangan hingga menghasilkan metode Automated Valuation Model (AVM) yaitu penilaian otomatis dengan alat yang mampu memberikan penilaian properti menggunakan pemodelan matematika yang digabungkan dengan database. Penerapan AVM menggunakan Jaringan Saraf Tiruan (JST) dapat memudahkan appraisal dengan adanya pembobotan pada masing-masing kriteria baik dari kriteria tanah dan bangunan yang akan dinilai. AVM yang didasarkan pada metodologi JST mencakup berbagai karakteristik properti sebagai input dan estimasi nilai pasar sebagai output. Salah satu metode JST yaitu backpropagation yang berperan dalam pelatihan jaringan dengan tujuan penyeimbangan antara kemampuan pengenalan pola dengan pemberian respon yang benar terhadap pola input serupa namun tidak sama persis dengan pola yang digunakan dalam pelatihan tersebut. Klasifikasi dari AVM dengan algoritma JST backpropagation yang diukur menggunakan Confusion Matrix menghasilkan accuracy sebesar 80%

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped upon decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review

Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that combining deep learning with symbolic reasoning will lead to stronger AI than either paradigm on its own. As successful as deep learning has been, it is generally accepted that even our best deep learning systems are not very good at abstract reasoning. And since reasoning is inextricably linked to language, it makes intuitive sense that Natural Language Processing (NLP), would be a particularly well-suited candidate for NeSy. We conduct a structured review of studies implementing NeSy for NLP, with the aim of answering the question of whether NeSy is indeed meeting its promises: reasoning, out-of-distribution generalization, interpretability, learning and reasoning from small data, and transferability to new domains. We examine the impact of knowledge representation, such as rules and semantic networks, language structure and relational structure, and whether implicit or explicit reasoning contributes to higher promise scores. We find that systems where logic is compiled into the neural network lead to the most NeSy goals being satisfied, while other factors such as knowledge representation, or type of neural architecture do not exhibit a clear correlation with goals being met. We find many discrepancies in how reasoning is defined, specifically in relation to human level reasoning, which impact decisions about model architectures and drive conclusions which are not always consistent across studies. Hence we advocate for a more methodical approach to the application of theories of human reasoning as well as the development of appropriate benchmarks, which we hope can lead to a better understanding of progress in the field. We make our data and code available on github for further analysis.Comment: Surve